Araştırma Makalesi
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Morphological parameters causing landslides: A case study of elevation

Yıl 2020, Cilt: 162 Sayı: 162, 197 - 224, 15.08.2020
https://doi.org/10.19111/bulletinofmre.649758

Öz

The history of landslide susceptibility maps goes back about 50 years. Hazard and risk maps later followed these maps. Inventory maps provide the source of all these. There are different parameters selected specially for each field in the literature as well as parameters selected because they are easy to produce and obtain data. This study tried to research the effect of elevation on landslides by reviewing the literature in detail. The used class ranges and elevation values were reviewed and applied to map sections selected from Turkey. By analyzing the results, the goal was to determine at which elevation ranges landslides occurred. The study tried to investigate the effect of the parameter of elevation using data from the literature. It works to compare the elevation values for map sections selected to compare with the literature. The study comprises two stages. The first step tried to acquire statistical data by researching the data from the literature. The data were investigated in the second stage. For this purpose, close to 1.500 studies prepared between 1967 and 2019 were reviewed. According to the literature, the parameter of was used in analyses because it is easy to produce and is morphologically effective.

Teşekkür

This study was carried out within scope of MMF.A4.18.017 BAP project

Kaynakça

  • Abedini, M., Ghasemyan, B., Mogaddam, M.H. 2017. Landslide susceptibility mapping in Bijar City, NW Province of Iran: A comparative study by logistic regression and AHP models. Environ Earth Science 292- 308.
  • Acharya, S., Pathak, D. 2017. Landslide hazard assessment between Besi Sahar and Tal area in Marsyangdi River Basin, West Nepal. Int. Journal of Advances in Remote Sensing and GIS 29-38.
  • Aghdam, I.N., Varzandeh, M.H.M., Pradhan, B. 2016. Landslide susceptibility mapping using an Ensemble Statistical İndex (Wi) and adaptive Neuro-Fuzzy Inference System (ANFIS) Model at Alborz Mountains (Iran). Environ Earth Sci 1-20.
  • Akıncı, H., Kılıçoğlu, C. 2015. Production of landslide susceptibility map of Atakum (Samsun) district. MÜHJEO’2015: National Engineering Geology Symposium, 3-5 September 2015, Trabzon.
  • Akıncı, H., Doğan, S., Kılıçoğlu, C., Keçeci, S.B. 2010. Production of landslide susceptibility map of Samsun province center. Electronic Journal of Map Technologies 13-27. Akıncı, H., Doğan, S., Kılıçoğlu C. 2011. Production of landslide susceptibility map of Samsun City Center by using frequency ratio method. TMMOB Surveying Engineers, 13th Turkey Scientific and Technical Conference 18-22 April 2011, Ankara.
  • Akıncı, H., Özalp-Yavuz, A., Özalp, M., Temuçin-Kılıçer, S., Kılıçoğlu, C., Everan, E. 2014. Production of landslide susceptibility maps using bayesian probability theorem. 5. Remote Sensing-GIS Symposium (Uzal-GIS), 14-17 Oct., 2014, İstanbul.
  • Althuwaynee, O.F., Pradhan, B., Lee, S. 2016. A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing 37-40.
  • Amirahmadi, A., Shiran, M., Zanganeh, Asadi, M., Keramati, F. 2017. Landslide susceptibility zonation using the fuzzy algebraic operators in GIS. Iran. J. Mater Environ Sci 50-59.
  • Anbalagan, R. 1992. Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering Geology 269-277.
  • Anbalagan, R., Singh, B. 1996. Landslide hazard and risk assessment mapping of mountainous terrains-a case study from Kumaun Himalaya, India. Engineering Geology 237-246.
  • Aniya, M. 1985. Landslide-susceptibility mapping in Amahata River Basin, Japan. Annals of the Association of American Geographers 102-114.
  • Ataol, M., Yeşilyurt, S. 2014. Identification of landslide risk zones along the Çankırı-Ankara (between Akyurt and Çankırı) state road. Istanbul University Faculty of Letters Department of Geography 51- 69.
  • Avcı, V. 2016a. Analysis of landslide succeptibility of Manav Stream Basin (Bingöl). The Journal of International Social Research 42-49.
  • Avcı, V. 2016b. Landslide susceptibility analysis of Esence Stream Basin (Bingöl) by weight- of- evidence method. International Journal of Social Science 287-310.
  • Avcı, V. 2016c. The landslide susceptibility analysis of the Gökdere Basin and its surrounding region (The Southwest of Bingöl) according to the frequency ratio method. Marmara Geographical Review 160-177.
  • Avcı, V., Günek, H. 2014. The distribution of active landslides in Karlıova Basin and surrounding (Bingöl) according to lithology, elevation, slope, inspection and NDVI Parts. International Journal of Social Science 445-464.
  • Ayalew, L., Yamagishi, H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 15- 31.
  • Ayalew, L., Yamagishi, H., Marui, H., Kanno, T. 2005. Landslides in Sado Island of Japan Part II. GIS- based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology 432-445.
  • Baeza, C., Corominas, J. 2001. Assessment of shallow landslides susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms 251-1263.
  • Bai, S.B., Wang, J., Lü, G.N., Zhou, P.G., Hou, S.S., Xu, S.N. 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 23-31.
  • Bai, S.B., Xu, Q., Jian, W., Zhou, P. 2013. Pre-conditioning factors and susceptibility assessments of Wenchuan Earthquake landslide at the Zhouqu Segment of Bailongjiang Basin. China Journal of the Geological Society of India 82-95.
  • Bai, S.B., Wang, J., Thiebes, B., Cheng, C., Chang, Z.Y. 2014. Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression. Environmental Earth Sciences 731–743.
  • Balamurugan, G., Ramesh, V., Touthang, M. 2016. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Nat Hazards 465–488.
  • Balteanu, D., Chendes, V., Sima, M., Enciu, P. 2010. A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology 102– 112.
  • Caniani, D., Pascale, S., Sdao, F., Sole, A. 2008. Neural networks and landslide susceptibility: A case study of the urban area of Potenza. Natural Hazards 55–72.
  • Carrara, A. 1983. Multivariate models for landslide hazard evaluation. Mathematical Geology 403–426.
  • Carrara, A., Cardinalı, M., Detti, R., Guzzetti, F., Pasqui, V., Reichenbach, P. 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms 427-445.
  • Chalkias, C., Ferentinou, M., Polykretis, C. 2014. GIS- based landslide susceptibility mapping on the Peloponnese Peninsula, Greece Geosciences 176- 190.
  • Chau, K. T., Chan, J. E. 2005. Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides 280–290.
  • Chauhan, S., Sharma, M., Arora, M.K., Gupta, N.K. 2010. Landslide susceptibility zonation through ratings derived from artificial neural network. International Journal of Applied Earth Observation and Geoinformation 340-350.
  • Chen, Z., Wang, J. 2007. Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Natural Hazards 75-89.
  • Chen, C.W., Saito, H., Oguchi, T. 2015. Rainfall intensity– duration conditions for mass movements in Taiwan, Progress in. Earth and Planetary Science 1–13.
  • Chen, S.C., Chang, C.C., Chan, H.C., Huang, L.M., Lin,L.L. 2013. Modeling typhoon event-induced landslides using GIS-based logistic regression: A case study of Alishan Forestry Railway, Taiwan. Math. Prob. Eng. URL: https://www.hindawi. com/journals/mpe/2013/728304/.
  • Chen, W., Chai, H., Sun, X., Wang, Q., Ding, X., Hong,H. 2016a. A GIS-based comparative study of frequency ratio, statistical index and weights- of-evidence models in landslide susceptibility mapping. Arab J Geosci 204-215.
  • Chen, W., Wang, J., Xie, X., Hong, H., Trung Van N., Bui,D. T., Wang, G., Li, X. 2016b. Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environ Earth Sci 1344-1350.
  • Chen, W., Pourghasemi, H. R., Kornejady, A., Zhang, N. 2017. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 314–327.
  • Chen, W., Pourghasemi, H. R., Kornejady, A., Xie, X. 2018. GIS-based landslide susceptibility evaluation using certainty factor and index of entropy ensembled with alternating decision tree models, In book: Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Advances in Natural and Technological Hazards Research 48-57.
  • Chen, W., Sun, Z., Han, J. 2019. Landslide susceptibility modeling using ıntegrated ensemble weights of evidence with logistic regression and random forest models. Applied Sciences 17-29.
  • Clerici, A., Perego, S., Tellini, C., Vescovi, P. 2006. A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology 941-961.
  • Coe, J.A., Michael, J.A., Crovelli, R.A., Savage, W.Z., Laprade, W.T., Nashem, W.D. 2004a. Probabilistic assessment of precipitation triggered landslides using historical records of landslide occurrence, Seattle, Washington. Environ Eng Geosc 103– 122.
  • Coe, J.A., Godt, J.W., Baum, R.L., Bucknam, R.C., Michael, J.A., 2004b. Landslide susceptibility from topography in Guatemala, In: Lacerda WA et al. (ed) Landslides, evaluation and stabilization. Proceedings of the 9th International Symposium on Landslides, Rio de Janeiro, 69–79.
  • Conforti, M., Pascale, S., Robustelli, G., Sdao, F. 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria Italy). Catena 236-250.
  • Creighton, R. 2006. A Report of the Irish Landslides Working Group. Geological Survey of Ireland 100-109.
  • Çellek, S. 2013. Landslide susceptibility analysis of Sinop- Gerze region. Doctora Thesis, KTU, Trabzon (unpublished).
  • Çellek, S., Bulut, F., Ersoy, H. 2015. Utilization and Application of AHP Method in Landslide Susceptibility Mapping Production (Sinop and its Surroundings), Research Article. Journal of Geological Engineering 59-90.
  • Çevik, E., Topal, T. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology 949-962.
  • Dağ, S. 2007. Landslide Susceptibility Analysis of Çayeli (Rize) and its Surranding by Statistical Methods. Doktorate Thesis, KTU, Trabzon, (unpublished).
  • Dağ, S., Bulut, F. 2012. Preparation of GIS-based landslide susceptibility maps: Çayeli (Rize, NE Türkey) Research Article. Journal of Geological Engineering 35-62.
  • Dai, F.C., Lee, C.F. 2001. Terrain-based mapping of landslide susceptibility using a geographical information system: A case study. Can Geotech J 911–923.
  • Dai, F.C., Lee, C.F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 213–228.
  • Dai, F.C., Lee, C.F. 2003. A spatiotemporal probabilistic modelling of storm induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Process Landforms 527–545.
  • Dehnavi, A., Aghdam, I.N., Pradhan, B., Varzandeh,M.H.M. 2015. A new hybrid model using step- wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy ınference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 122–148.
  • Demir, G. 2016. Landslide susceptibility assessment of the 1 part of the North Anatolian Fault Zone (Turkey) by GIS-based frequency ratio and index of entropy models. Nat. Hazards Earth Syst Sci 327-340.
  • Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., Althuwaynee, O.F. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat. Hazards 135–165.
  • Ding, Q., Chen, W., Hong, H. 2017. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping, Geocarto International.URL:https://www.researchgate.net/publication/298425978_Application_of_frequency_ratio_weights_of_evidence_and_evidential_belief_function_models_in_landslide_susceptibility_mapping
  • Dou, J., Yamagishi, H., Xu, Y., Zhu, Z., Yunus, A.P. 2017. Characteristics of the torrential rainfall- ınduced shallow landslides by typhoon bilis, in July 2006, using remote sensing and GIS. In book:GISLandslidePublisher:SpringerJapan,URL:https://www.researchgate.net/publication/317041333_Characteristics_of_the_Torrential_RainfallInduced_Shallow_Landslides_by_Typhoon_Bilis_in_July_2006_ Using_Remote_Sensing_and_GIS
  • Dölek, İ., Avcı, V. 2016. Determination of areas with landslide susceptibility in Arguvan district (Malatya province) and its surrounding by multi- criteria decision analysis method (MDAM). The Journal of Academic Social Science 106-129.
  • Dragicevi, C.S., Lai, T., Balram, S. 2015. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International 114–125.
  • Duman, T.Y., Çan, T., Gökçeoğlu, C., Nefeslioğlu, H.A., Sönmez, H. 2006. Application of logistic regression for landslide susceptibility zoning of Çekmece Area (İstanbul, Turkey). Environmental Geology 241-256.
  • Eker, A.M., Dikmen, M., Cambazoğlu, S., Akgün, H. 2012. Application of artificial neural network and logistic regression methods to landslide susceptibility mapping and comparison of the results for the Ulus district, Bartın. Journal of the Faculty of Engineering and Architecture of Gazi University 163-173.
  • Elkadiri, R., Sultan, M., Youssef, A.M., Elbayoumi, T., Chase, R., Bulkhi, A.B., Al-Katheeri, M.M. 2014. A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4818–4835.
  • Ercanoğlu, M. 2003. Production of landslide susceptibility maps by fuzzy logic and statistical methods: Western Black Sea Region (Kumluca-Yenice). Doctora Thesis, Hacettepe University, Ankara, (unpublished).
  • Ercanoğlu, M. 2005. Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks. Natural Hazards Earth System Science 979–992.
  • Ercanoğlu, M., Gökceoğlu, C. 2002. Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology 720–730.
  • Ercanoğlu, M., Gökçeoğlu, C. 2004. Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology 229–250.
  • Ercanoğlu, M., Gökceoğlu, C., Van Asch, Th. W.J. 2004. Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards 1–23.
  • Ercanoğlu, M., Kasmer, O., Temiz, N. 2008. Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment 565–578.
  • Erener, A., Lacasse, S., 2007. Landslide Susceptibility Mapping Using GIS. TMMOB Chamber of Survey and Cadastre Engineers National Geographic Information Systems Congress, KTÜ, Trabzon.
  • Fenghuan, S., Peng, C., Jianqiang, Z., Lingzhi, X., 2010. Susceptibility assessment of landslides caused by the Wenchaun earthquake using a logistic regression model. Journal of Mountain Science 234-245.
  • Fernandez, T., Irigaray, C., El Hamdouni, R., Chacon, J. 2003. Methodology for landslide susceptibility mapping by means of a GIS. Application to the contraviesa area (Granada, Spain). Nat. Hazards 297–308.
  • Fernandez, T., Jimenez, J., Fernandez, P., El Hamdouni, R., Cardenal, F.J., Delgado, J., Irigaray, C., Chacon, J. 2008. Automatic detection of landslide features with remote sensing techniques in the Betic Cordilleras (Granada, Southern Spain). Int Soc Photogramme, 351-356.
  • Gallart, F., Clotet, N. 1988. Some aspects of the geomorphic processes triggered by an extreme rainfall event: The November 1982 flood in The Eastern Pyrenees. Catena 79–95. Gattinoni, P. 2009. Parametrical landslide modeling for the hydrogeological susceptibility assessment: from the Crati Valley to the Cavallerizzo landslide (Southern Italy). Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards 161-178.
  • Goetz, J.N., Brenning, A., Petschko, H., Leopold, P. 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput. Geosci. 1–11.
  • Gomez, H., Kavzoğlu, T. 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology 11–27.
  • Gorsevski, P.V., Jankowski, P. 2008. Discerning landslide susceptibility using rough sets. Computers, Environment and Urban Systems 53-65.
  • Gorsevski, P.V., Donevska, K.R., Mitrovski, C.D., Frizado, J.P. 2012. Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: A case study using ordered weighted average. Waste Management 287-296.
  • Gökçeoğlu, C., Ercanoğlu, M. 2001. Uncertainties on the parameters employed in preparation of landslide susceptibility maps. Bulletin of Earth Sciences Application and Research Centre of Hacettepe University, 189-206.
  • Gökçeoğlu, C., Sönmez, H., Nefeslioğlu, H.A., Duman, T.Y., Çan, T. 2005. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide–susceptibility map of its near vicinity. Engineering Geology 65–83.
  • Görcelioğlu, E. 2003. Flood and avalanche control. Istanbul University Publication Faculty of Forestry Publication, 473-490.
  • Görüm, T. 2006. Landslide susceptibility analysis with geographic information systems and statistical methods: Melen Gorge and near vicinty. İstanbul University, Master Thesis, Istanbul (unpublished).
  • Gritzner, M.L., Marcus, W.A., Aspinall, R., Custer, S.G. 2001. Assessing landslide potential using GIS, soil wetness modelling and topographic attributes. Payette River, Idaho, Geomorphology 149-165.
  • Gruber, S., Haeberli, W. 2007. Permafrost in steep bedrock slopes and its temperature-related destabilization following climate change. J Geophys Res 112- 130, URL: https://agupubs.onlinelibrary.wiley. com/doi/10.1029/2006JF000547
  • Hasekioğulları, G.D. 2011. Assessment of parameter effects in producing landslide susceptibility maps. Master Thesis, Hacettepe University, Ankara (unpublished). He, S., Li, D., Wu, Luo, Y. 2011. Study on the rainfall and aftershock threshold for debris flow of post- earthquake. J. Mountain Sci 750–756.
  • Hong, H., Chen, W., Xu, C., Youssef, A.M., Pradhan, B., Tien, Bui, D. 2017a. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int 139–154.
  • Hong, H., Ilia, I., Tsangaratos, P., Chen, W., Xu, C. 2017b. A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 1–16.
  • Ilia, I., Tsangaratos, P. 2016. Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 379–397.
  • Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J., Sattarian, A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 909–926.
  • Jaafari, A., Najafi, A., Rezaeian, J., Sattarian, A. 2015a. Modeling erosion and sediment delivery from unpaved roads in the north mountainous forest of Iran. International Journal on Geomathematics 343–350.
  • Jaafari, A., Najafi, A., Rezaeian, J., Sattarian, A., Ghajar, I. 2015b. Planning road networks in landslide-prone areas: A case study from the northern forests of Iran. Land Use Policy 198–208.
  • Jebur, M.N., Pradhan, B., Tehrany, M.S. 2015. Manifestation of LiDAR derived parameters in spatial prediction of landslides using a novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J Sel Top Appl Earth Obs Remote Sens 674-689.
  • Jimenez-Peralvarez, J.D., Irigaray, C., El Hamdouni, R., Chacon, J. 2009. uilding models for automatic landslide-susceptibility analysis, mapping and validation in ArcGIS. Nat Hazards, 571 – 590.
  • Juang, C.H., Lee, D.H., Sheu, C. 1992. Mapping slope failure potential using fuzzy sets. Journal of Geotechnical Engineering ASCE 475–494.
  • Kamp, U., Growley, B.J., Khattak, G.A., Owen, L.A. 2008. GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 631–642.
  • Kavzoğlu, T., Çölkesen, İ. 2010. Classification of Satellite Images Using Decision Trees: Kocaeli Case. Electronic Journal of Map Technologies 36-45.
  • Kavzoğlu, T., Çölkesen, İ., Şahin, E.K. 2012. Investigation of the Effects of Factors Used in Production of Landslide Susceptibility Maps: A Case Study in Düzköy. IV. Remote Sensing and Geographic Information Systems Symposium (UZAL-GIS),Oct., 2012, Zonguldak.
  • Kavzoğlu, T., Şahin, E.K., Çölkesen, İ. 2014. Factor Selection based on Chi-Square Test in Landslide Sensitivity Analysis. V. Remote Sensing and Geographical Information Systems Symposium (UZAL-GIS), 14-17 Oct.,2014, İstanbul.
  • Kornejady, A., Heidari, K., Nakhavali, M. 2015. Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran Environ Resour Res 85–109.
  • Kornejady, A., Ownegh, M., Bahremand, A. 2017a. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 144–162.
  • Kornejady, A., Ownegh, M., Rahmati, O., Bahremand, A. 2017b. Landslide susceptibility assessment using three bivariate models considering the new topo- hydrological factor: HAND. Geocarto Int 1155– 1185.
  • Koukis, G., Ziourkas, C. 1991. Slope Instability Phenomena in Greece: A Statistical Analysis. Bulletin of International Association of Engineering Geologists 47-60.
  • Kouli, M., Loupasakis, C., Soupios, P., Rozos, D., Vallianatos, F. 2014. Landslide susceptibility mapping by comparing the WLC and WofE mutli- criteria methods in the West Crete Island, Greece. Environ Earth Sci.
  • Lan, H.X., Zhou, C.H., Wang, L.J., Zhang, H.Y., Li, R.H., 2004. Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China Eng Geol 109–128.
  • Lee, S., Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 33–41.
  • Lee, S., Choi, J., Min, K. 2002. Landslide susceptibility analysis and verication using the bayesian probability model. Enviromental Geology 120- 131.
  • Leonardi, G., Palamaraa, R., Ciriannia, F. 2016. Landslide Susceptibility mapping using a fuzzy approach procedia engineering, World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium, WMCAUS, 380–387.
  • Liu, C., Li, W., Wu, H. 2013. Susceptibility evaluation and mapping of China’s landslides based on multi- source data. Natural Hazards 1477–1495.
  • Liu, S., Wu, Y. 2016. Landslide susceptibility mapping in the Gangu County, China using artificial neural network and Gis. Bund 7614-7628.
  • Mashari, S., Solaimani, K., Omidvar, E. 2012. Landslide susceptibility mapping using multiple regression and gıs tools in Tajan Basin, north of Iran.URL:https://www.researchgate. net/publication/311921212_Landslide_ Susceptibility_Mapping_ Using_Multiple_ Regression_and_GIS_Tools_in_Tajan_Basin_ North_of_Iran
  • Mazman, T. 2005. Landslide susceptibility assessment in Kumluca (SE Bartın) watershed by geoeraphic information systems and statistical analysis methods. Master Thesis, Çukurova University, Adana (unpublished).
  • Meng, Q., Miao, F., Zhen, J., Wang, X., Wang, A., Peng, Y., Fan, Q. 2015. GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: A case study from Wolong Giant Panda Natural Reserve, China Bull Eng Geol Environ.
  • Meng, X., Pei, X., Liu, Q., Zhang, X., Hu, Y. 2016. GIS-based environmental assessment from three aspects of geology, ecology and society along the road from Dujiangyan to Wenchuan, Mt Res 110–120.
  • Mohammady, M., Pourghasemi, H.R., Pradhan, B. 2012. Landslide susceptibility mapping at Golestan Province Iran: A comparison between frequency ratio, Dempster-Shafer, andweights of evidence models. J Asian Earth Sci 221-230.
  • Moore, I.D., Grayson, R.B., Ladson, A.R. 1991. Digital terrain modelling: A review of hydrological, geomorphological and biological applications. Hydrological Processes 23-30.
  • Moradi, S., Rezaei, M. 2014. A GIS-based comparative study of the analytic hierarchy process, bivariate statistics and frequency ratio methods for landslide susceptibility mapping in part of the Tehran metropolis, Iran J Geope 45-61.
  • Myronidis, D., Papageorgiou, C., Theophanous, S. 2016. Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat. Hazards 245–263.
  • Nagarajan, R., Roy, A., Vinod, Kumar, R., Mukherjee, A., Khire, M.V. 2000. Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bulletin of Engineering Geology and the Environment 275–287.
  • Nefeslioğlu, H., Gökceoğlu, C., Sönmez, H. 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology 171- 191. Nourani, V., Pradhan, B., Ghaffari, H., Sharifi, S.S. 2014. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression and artificial neural network models. Nat Hazards 523–547.
  • Ochoa, G. 1978. La influencia de la altitud sobre algunas propiedades fisico-quı´micas de los suelos de los Andes venezolanos. Revista Geograifica 56–72.
  • Oh, H.J., Pradhan, B. 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers&Geosciences 1264-1276.
  • Opiso, E.M., Puno, G.R., Alburo, J.L.P., Detalla, A.L. 2016. Landslide susceptibility mapping using GIS and FR method along the Cagayan de Oro-Bukidnon- Davao City route corridor, Philippines. KSCE Journal of Civil Engineering 2506–2512.
  • Özdemir, A. 2009. Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 1675–1686.
  • Özdemir, A., Altural, T. 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 180–197.
  • Özşahin, E. 2015. Landslide susceptibility analysis by geographical information systems: the case of Ganos Mount (Tekirdağ). Electronic Journal of Map Technologies 47-63.
  • Özşahin, E., Kaymaz, Ç.K. 2013. Landslide susceptibility analysis of camili (Macahel) Biosphere Reserve Area (Artvin, NE Turkey). Turkish Studies - International Periodical for The Languages, Literature And History Of Turkish Or Turkic, 471-493.
  • Pachauri, A.K., Pant, M. 1992. Landslide hazard mapping based on geological attributes. Engineering Geology 81-100.
  • Pachauri, A.K., Gupta, P.V., Chander, R. 1998. Landslide zoning in a part of the Garhwal Himalayas. Env Geol 325-334.
  • Padrones, J. T., Ramos, N.T., Dimalanta, C.B., Queaño, K.L., Faustino-Eslava, D.V., Yumul, G.P. Jr., Watanabe, K. 2017. Landslide Susceptibility mapping in a geologically complex terrane: A case study from northwest Mindoro, Philippines Manila. Journal of Science 25–44.
  • Park, S., Jeon, S., Choi, C. 2010. Mapping urban growth probability in South Korea: Comparison of frequency ratio, analytic hierarchy process, and logistic regression models and use of the environmental conservation value assessment. Landscape and Ecological Engineering.
  • Pawluszek, K., Borkowski, A. 2017. Impact of DEM- derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Natural Hazards 919– 952.
  • Peng, L., Niu, R., Huang, B., Wu, X., Zhao, Y., Ye, R. 2014.Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology 287–301.
  • Pham, B.T., Tien, Bui, D., Indra, P., Dholakia, M. 2015. Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency ratio method. Int J Eng Res Technol 338–344.
  • Pham, B.T., Bui, D.T., Dholakia, M., Prakash, I., Pham, H.V. 2016. A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall- induced landslides in a tropical cyclones area. Geotech Geol Eng 1–18.
  • Pham, B.T., Bui, D.T., Prakash, I., Dholakia, M.B. 2017. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 52–63.
  • Pourghasemi, H.R., Gökçeoğlu, C., Pradhan, B., Deylami, Moezzi, K. 2012a. Landslide susceptibility mapping using a spatial multicriteria evaluation model at Haraz Watershed Iran, In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements, Springer Berlin, 23–49.
  • Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C. 2012b. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed Iran. Nat Hazards URL:https://www.researchgate.net/ publication/230875072_Application_of_fuzzy_ logic_and_analytical_hierarchy_process_AHP_ to_landslide_susceptibility_mapping_at_Haraz_ watershed_Iran
  • Pourghasemi, H.R., Mohammady, M., Pradhan, B. 2012c. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97, 71–84.
  • Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C. 2012d. Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. AEROTECH IV– 2012, Appl Mech Mater, 486–491. Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C., Mohammadi, M., Moradi, H.R. 2012e. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 2351–2365.
  • Pourghasemi, H.R., Moradi, H.R., Aghda, S.F. 2013a. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 749–779.
  • Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C., Mohammadi, M., Moradi, H.R., 2013b. Application of weights-of-evidence and certainty factor models and their comparison inlandslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 2351.
  • Pourghasemi, H.R., Beheshtirad, M. 2015. Assessment of a data–driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int 662–685.
  • Pourghasemi, H.R., Kerle, N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran province, Iran. Environmental Earth Sciences 185.
  • Pourghasemi, H.R., Rossi, M. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 609–633.
  • Pourtaghi, Z.S., Pourghasemi, H.R., Rossi, M. 2014. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth 1515–1533.
  • Pradhan, A.M.S., Kim, Y.T. 2014. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Natural Hazards 1189–1217.
  • Pradhan, A.M.S, Kim, Y.T. 2015. Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events. Environ. Earth Sci 5761–5771.
  • Pradhan, A.M.S., Kim, Y.T. 2017. Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon, Seoul, Korea. Bull Eng Geol Environ 1263–1279.
  • Pradhan B, Abokharima, M.H., Jebur, M.N., Tehrany, M.S. 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using, the evidential belief function model in GIS. Nat Hazards 112. Raja, N. B., Çiçek, I., Türkoğlu, N., Aydın, O., Kawasaki, A. 2017. Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Natural Hazards 1323-1346.
  • Ramesh, V., Anbazhagan, S. 2015. Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci 8009–8021.
  • Ray, R.L., Jacobs, J.M. 2007. Relationships among remotely sensed soil moisture, precipitation and landslide events. Natural Hazards 211–222.
  • Rozos, D.E.L., Skias, P.S., Tsangaratos, P. 2008. An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa County. Landslides 261-270.
  • Rozos, D., Bathrellos, G.D., Skilodimou, H.D. 2010. Landslide susceptibility mapping of the northeastern part of Achaia Prefecture using Analytical Hierarchical Process and GIS techniques. Bulletin of the geological society of Greece, Proceeding of the12th International Congress, Patras may, XLIII, 1637-1646.
  • Rozos, D., Bathrellos, G.D., Skilodimou, H.D. 2011. Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: A case study from the Eastern Achaia County of Peloponnesus Greece. Environ Earth Sci 49–63.
  • Sabatakakis, N., Koukis, G., Vassiliades, E., Lainas, S. 2013. Landslide susceptibility zonation in Greece. Natural Hazards 523–543.
  • Saadatkhahi, N., Kassimi, A., Lee, M. L. 2014. Qualitative and quantitative landslide susceptibility assessments in Hulu Kelang area, Malaysia. EJGE, 545-563. URL: http://www.ejge.com/2014/ Ppr2014.047mar.pdf
  • Sadr, M. P., Abbas, M., Bashir, S.S. 2014. Landslide susceptibility mapping of Komroud sub-basin using fuzzy logic approach. Geodyn Res Int Bull 14–27.
  • Sancar, C. 2000. Economy-ecology sensitive planning model and GIS for determination of urban development areas and planning. Phd Thesis, KTU, Trabzon (unpublished).
  • Schicker, R., Moon, V. 2012. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 40-57.
  • Sezer, E.A., Pradhan, B., Gökçeoğlu, C. 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications 8208-8219. Shrestha, S., Kang, T.S., Suwal, M. 2017. An Ensemble model for co-seismic landslide susceptibility using gıs and 29 random forest method. ISPRS International Journal of Geo-Information 365.
  • Simon, N. de Róiste M., Crozier, M., Rafek, A.G. 2017. Representing Landslides as Polygon (Areal) or Points? How Different Data Types Influence the Accuracy of Landslide Susceptibility Maps, Sains Malaysiana, 27-34.
  • Sujatha, E.R., Kumaravel, P., Rajamanickam, G.V. 2014. Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bull Engg Geol Environ 147–161.
  • Süzen, M.L., Doyuran, V. 2004a. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology 303-321.
  • Süzen, M.L., Doyuran, V. 2004b. A comparison of the GIS based landslide susceptibility assessment methods: Multivariate Versus Bivariate. Environmental Geology 665-679.
  • Süzen, M.L., Kaya, B.Ş. 2011. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth 1–18.
  • Tangestani, M.H. 2003. Landslide susceptibility mapping using the fuzzy gamma operation in a GIS, Kakan catchment area, Iran. Map India Conference. URL: file:/// C:/ Users/ Dekan/ Downloads/ Landslide susceptibility_mapping_using thefuzzyg.pdf
  • Tangestani, M. H. 2004. Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Australian Journal of Earth Sciences 439–450.
  • Tanoli, J. I., Ningsheng, C., Regmi, A. D., Jun, L. 2017. Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arabian Journal of Geosciences 10-13.
  • Tazik, E., Jahantab, Z., Bakhtiari, M., Rezae, A., Alavipanah, S. K. 2014. Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain Basin. International Conference on Geospatial Information Research (GI Research) 15-17 November 2014, Tehran, Iran.
  • Tsangaratos, P., Ilia, I. 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 164–179. Umar, Z., Pradhan, B., Ahmad, A., Neamah, Jebur, M., Shafapour, Tehrany, M. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera province, Indonesia. Catena 124–135.
  • Vivas, L. 1992. Los Andes Venezolanos, Academia Nacional de la Historia, Caracas.Wan, S., Lei, T. C., Chou, T.Y. 2012. Alandslide expert system: image classification through integration of data mining approaches for multi-category analysis. International Journal of Geographical Information Science 747-770.
  • Wang, H. Q., He, J., Liu, Y., Sun, S. 2016. Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 422.
  • Wang, L., Guo, M., Sawada, K., Lin, J., Zhang, J. 2015. Landslide susceptibility mapping in Mizunami city, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Geomorphology 271–282.
  • Wang, Y., Zhao, B., Li, J. 2017. Mechanism of the catastrophic June 2017 landslide at Xinmo Village, Songping River, Sichuan Province, China. Landslides 333–345.
  • Wu, Y., Ke, Y. 2016. Landslide susceptibility zonation using GIS and evidential belief function model. Arabian Journal of Geosciences 697.
  • Wu, Y., Li, W., Liu, P., Bai, H., Wang, Q., He, J., Liu, Y., Sun, S. 2016. Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 422.
  • Xu, C., Xu, X.W. 2012. Spatial prediction models for seismic landslides based on support vector machine and varied kernel functions: a case study of the 14 April 2010 Yushu earthquake in China. Chin J Geophys 666–679.
  • Xu, C., Xu, X.W. 2013. Controlling parameter analyses and hazard mapping for earthquake triggered- landslides: an example from a square region in Beichuan County, Sichuan Province, China. Arab J Geosci 3827–3839.
  • Xu, C., Xu, X., Shen, L., Yao, Q., Tan, X., Kang, W., Ma, S., Wu, X., Cai, J., Gao, M., Li, K. 2016a. Optimized volume models of earthquake-triggered landslides. Scientific Reports 6, 29797.
  • Xu, C., Xu, X., Tian, Y., Shen, L., Yao, Q., Huang, X., Ma, J., Chen, X., Ma, S. 2016b. Two comparable earthquakes produced greatly different coseismic landslides: The 2015 Gorkha, Nepal and 2008 Wenchuan, China events. Journal of Earth Science 1008-1015.
  • Yalçın, A., Reis, S., Aydınoğlu, A.C., Yomralıoğlu, T. 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 274-287.
  • Yang, Z.H., Lan, H.X., Gao, X., Li, L.P., Meng, Y.S.,Wu, Y.M. 2015. Urgent landslide susceptibility assessment in the 2013 Lushan earthquake- impacted area, Sichuan Province, China. Nat Hazards 2467–2487.
  • Yao, X., Tham, L.G., Dai, F.C. 2008. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China. Geomorphology 572-582.
  • Yılmaz, Ç., Topal, T., Süzen, M.L. 2012. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Sciences 2161-2178.
  • Yılmaz, I. 2009a. A case study from Koyulhisar (Sivas- Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 297–306.
  • Yılmaz, I. 2009b. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Comput Geosci 1125–1138.
  • Yılmaz, I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 821–836.
  • Yılmaz, I., Keskin, İ. 2009. GIS based statistical and physical approaches to landslide susceptibility mapping (Şebinkarahisar, Turkey). Bulletin of Engineering Geology and the Environment 459-471.
  • Youssef, A.M. 2015. Landslide susceptibility delineation in the Ar-Rayth Area, Jizan, Kingdom of Saudi Arabia, by using analytical hierarchy process, frequency ratio,and logistic regression models. Environ Earth Sci 8499–8518.
  • Youssef, A.M., Al-Kathery, M., Pradhan, B. 2014a. Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 113–134.
  • Youssef, A. M., Al-Kathery, M., Pradhan, B., Elsahly, T. 2014b. Debris flow impact assessment along the Al-Raith Road, Kingdom of Saudi Arabia, using remote sensing data and field investigations.Geomat Nat Hazards Risk 7-20.
  • Youssef, A. M., Pradhan, B., Jebur, M. N., El-Harbi, H. M., 2014c. Landslide susceptibility mappin gusing ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73-80.
  • Youssef, A.M., Al-Kathery, M., Pradhan, B. 2015. Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 113–134.
  • Yüksel, N. 2007. Usage of statistical techniques and artificial neural networks in producing landslide susceptibility maps based on geographical information systems: Kumluca-Ulus (Bartın) region. Phd Thesis, Hacettepe University, Ankara (unpublished).
  • Zare, M., Jouri, M.H., Salarian, T., Askarizadeh, D., Miarrostami, S. 2014. Comparing of bivariate statistic, AHP and combination methods to predict the landslide hazard in northern aspect of Alborz Mt. (Iran). Intl J Agri Crop Sci 543-554.
  • Zhang, J., Yin, K., Wang, J., Liu, L., Huang, F. 2016a. Evaluation of landslide susceptibility for Wanzhou district of three Gorges reservoir. Chinese Journal of Rock Mechanics and Engineering 35.
  • Zhang, J. Q., Liu, R. K., Deng, W., Khanal, N. R., Gurung,D. R., Sri, Ramachandra, Murthy, M., Wahid, S. 2016b. Characteristics of landslide in Koshi River Basin, Central Himalaya. Journal of Mountain Science 1711–1722.
  • Zhang, K., Wu, X., Niu, R., Yang, K., Zhao, L. 2017. The Assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir Area, China. Environ Earth Sci 405. Zhang, M.S., Dong, Y., Sun, P.P. 2012. Impact of reservoir impoundment-caused groundwater level changes on regional slope stability: a case study in the Loess Plateau of Western China. Environ Earth Sci 1715–1725.
  • Zhao, W., Li, A., Deng, W. 2014. Surface energy fluxes estimation over the South Asia subcontinent through assimilating MODIS/TERRA satellite data with In Situ observations and GLDAS product by SEBS model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3704–3712.
  • Zhu, H. H., Shi, B., Yan, J. F., Zhang, J., Zhang, C. C., Wang, B. J. 2014. Fiber Bragg grating-based performance monitoring of a slope model subjected to seepage. Smart Mater Struct 23, 095027. URL: https://iopscience.iop.org/ article/10.1088/0964-1726/23/9/095027
  • Zhuang, J., Peng, C., Wang, G., Chen, X., Iqbal, J., Guo,X. 2015. Rainfall thresholds for the occurrence of debris flows in the Jiangjia Gully, Yunnan Province, China. Engineering Geology, 195. URL:https:// www.researchgate.net/publication/281746840_Rainfall_thresholds_for_the_occurrence_of_debris_flows_in_the_Jiangjia_Gully_Yunnan_ Province_China
  • Zolotraev, W.H. 1976. Present day problems in the engineering geological investigation of landslides, falls and mudflows in mountainous folded regions. In: Hutchinson JN (ed) Geological factors and mechanism involved in the development of landslides, falls and mudflows. UNESCO, Paris, 5-34.
Yıl 2020, Cilt: 162 Sayı: 162, 197 - 224, 15.08.2020
https://doi.org/10.19111/bulletinofmre.649758

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Kaynakça

  • Abedini, M., Ghasemyan, B., Mogaddam, M.H. 2017. Landslide susceptibility mapping in Bijar City, NW Province of Iran: A comparative study by logistic regression and AHP models. Environ Earth Science 292- 308.
  • Acharya, S., Pathak, D. 2017. Landslide hazard assessment between Besi Sahar and Tal area in Marsyangdi River Basin, West Nepal. Int. Journal of Advances in Remote Sensing and GIS 29-38.
  • Aghdam, I.N., Varzandeh, M.H.M., Pradhan, B. 2016. Landslide susceptibility mapping using an Ensemble Statistical İndex (Wi) and adaptive Neuro-Fuzzy Inference System (ANFIS) Model at Alborz Mountains (Iran). Environ Earth Sci 1-20.
  • Akıncı, H., Kılıçoğlu, C. 2015. Production of landslide susceptibility map of Atakum (Samsun) district. MÜHJEO’2015: National Engineering Geology Symposium, 3-5 September 2015, Trabzon.
  • Akıncı, H., Doğan, S., Kılıçoğlu, C., Keçeci, S.B. 2010. Production of landslide susceptibility map of Samsun province center. Electronic Journal of Map Technologies 13-27. Akıncı, H., Doğan, S., Kılıçoğlu C. 2011. Production of landslide susceptibility map of Samsun City Center by using frequency ratio method. TMMOB Surveying Engineers, 13th Turkey Scientific and Technical Conference 18-22 April 2011, Ankara.
  • Akıncı, H., Özalp-Yavuz, A., Özalp, M., Temuçin-Kılıçer, S., Kılıçoğlu, C., Everan, E. 2014. Production of landslide susceptibility maps using bayesian probability theorem. 5. Remote Sensing-GIS Symposium (Uzal-GIS), 14-17 Oct., 2014, İstanbul.
  • Althuwaynee, O.F., Pradhan, B., Lee, S. 2016. A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing 37-40.
  • Amirahmadi, A., Shiran, M., Zanganeh, Asadi, M., Keramati, F. 2017. Landslide susceptibility zonation using the fuzzy algebraic operators in GIS. Iran. J. Mater Environ Sci 50-59.
  • Anbalagan, R. 1992. Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering Geology 269-277.
  • Anbalagan, R., Singh, B. 1996. Landslide hazard and risk assessment mapping of mountainous terrains-a case study from Kumaun Himalaya, India. Engineering Geology 237-246.
  • Aniya, M. 1985. Landslide-susceptibility mapping in Amahata River Basin, Japan. Annals of the Association of American Geographers 102-114.
  • Ataol, M., Yeşilyurt, S. 2014. Identification of landslide risk zones along the Çankırı-Ankara (between Akyurt and Çankırı) state road. Istanbul University Faculty of Letters Department of Geography 51- 69.
  • Avcı, V. 2016a. Analysis of landslide succeptibility of Manav Stream Basin (Bingöl). The Journal of International Social Research 42-49.
  • Avcı, V. 2016b. Landslide susceptibility analysis of Esence Stream Basin (Bingöl) by weight- of- evidence method. International Journal of Social Science 287-310.
  • Avcı, V. 2016c. The landslide susceptibility analysis of the Gökdere Basin and its surrounding region (The Southwest of Bingöl) according to the frequency ratio method. Marmara Geographical Review 160-177.
  • Avcı, V., Günek, H. 2014. The distribution of active landslides in Karlıova Basin and surrounding (Bingöl) according to lithology, elevation, slope, inspection and NDVI Parts. International Journal of Social Science 445-464.
  • Ayalew, L., Yamagishi, H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 15- 31.
  • Ayalew, L., Yamagishi, H., Marui, H., Kanno, T. 2005. Landslides in Sado Island of Japan Part II. GIS- based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology 432-445.
  • Baeza, C., Corominas, J. 2001. Assessment of shallow landslides susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms 251-1263.
  • Bai, S.B., Wang, J., Lü, G.N., Zhou, P.G., Hou, S.S., Xu, S.N. 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 23-31.
  • Bai, S.B., Xu, Q., Jian, W., Zhou, P. 2013. Pre-conditioning factors and susceptibility assessments of Wenchuan Earthquake landslide at the Zhouqu Segment of Bailongjiang Basin. China Journal of the Geological Society of India 82-95.
  • Bai, S.B., Wang, J., Thiebes, B., Cheng, C., Chang, Z.Y. 2014. Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression. Environmental Earth Sciences 731–743.
  • Balamurugan, G., Ramesh, V., Touthang, M. 2016. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Nat Hazards 465–488.
  • Balteanu, D., Chendes, V., Sima, M., Enciu, P. 2010. A country-wide spatial assessment of landslide susceptibility in Romania. Geomorphology 102– 112.
  • Caniani, D., Pascale, S., Sdao, F., Sole, A. 2008. Neural networks and landslide susceptibility: A case study of the urban area of Potenza. Natural Hazards 55–72.
  • Carrara, A. 1983. Multivariate models for landslide hazard evaluation. Mathematical Geology 403–426.
  • Carrara, A., Cardinalı, M., Detti, R., Guzzetti, F., Pasqui, V., Reichenbach, P. 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms 427-445.
  • Chalkias, C., Ferentinou, M., Polykretis, C. 2014. GIS- based landslide susceptibility mapping on the Peloponnese Peninsula, Greece Geosciences 176- 190.
  • Chau, K. T., Chan, J. E. 2005. Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides 280–290.
  • Chauhan, S., Sharma, M., Arora, M.K., Gupta, N.K. 2010. Landslide susceptibility zonation through ratings derived from artificial neural network. International Journal of Applied Earth Observation and Geoinformation 340-350.
  • Chen, Z., Wang, J. 2007. Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Natural Hazards 75-89.
  • Chen, C.W., Saito, H., Oguchi, T. 2015. Rainfall intensity– duration conditions for mass movements in Taiwan, Progress in. Earth and Planetary Science 1–13.
  • Chen, S.C., Chang, C.C., Chan, H.C., Huang, L.M., Lin,L.L. 2013. Modeling typhoon event-induced landslides using GIS-based logistic regression: A case study of Alishan Forestry Railway, Taiwan. Math. Prob. Eng. URL: https://www.hindawi. com/journals/mpe/2013/728304/.
  • Chen, W., Chai, H., Sun, X., Wang, Q., Ding, X., Hong,H. 2016a. A GIS-based comparative study of frequency ratio, statistical index and weights- of-evidence models in landslide susceptibility mapping. Arab J Geosci 204-215.
  • Chen, W., Wang, J., Xie, X., Hong, H., Trung Van N., Bui,D. T., Wang, G., Li, X. 2016b. Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environ Earth Sci 1344-1350.
  • Chen, W., Pourghasemi, H. R., Kornejady, A., Zhang, N. 2017. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 314–327.
  • Chen, W., Pourghasemi, H. R., Kornejady, A., Xie, X. 2018. GIS-based landslide susceptibility evaluation using certainty factor and index of entropy ensembled with alternating decision tree models, In book: Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Advances in Natural and Technological Hazards Research 48-57.
  • Chen, W., Sun, Z., Han, J. 2019. Landslide susceptibility modeling using ıntegrated ensemble weights of evidence with logistic regression and random forest models. Applied Sciences 17-29.
  • Clerici, A., Perego, S., Tellini, C., Vescovi, P. 2006. A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology 941-961.
  • Coe, J.A., Michael, J.A., Crovelli, R.A., Savage, W.Z., Laprade, W.T., Nashem, W.D. 2004a. Probabilistic assessment of precipitation triggered landslides using historical records of landslide occurrence, Seattle, Washington. Environ Eng Geosc 103– 122.
  • Coe, J.A., Godt, J.W., Baum, R.L., Bucknam, R.C., Michael, J.A., 2004b. Landslide susceptibility from topography in Guatemala, In: Lacerda WA et al. (ed) Landslides, evaluation and stabilization. Proceedings of the 9th International Symposium on Landslides, Rio de Janeiro, 69–79.
  • Conforti, M., Pascale, S., Robustelli, G., Sdao, F. 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria Italy). Catena 236-250.
  • Creighton, R. 2006. A Report of the Irish Landslides Working Group. Geological Survey of Ireland 100-109.
  • Çellek, S. 2013. Landslide susceptibility analysis of Sinop- Gerze region. Doctora Thesis, KTU, Trabzon (unpublished).
  • Çellek, S., Bulut, F., Ersoy, H. 2015. Utilization and Application of AHP Method in Landslide Susceptibility Mapping Production (Sinop and its Surroundings), Research Article. Journal of Geological Engineering 59-90.
  • Çevik, E., Topal, T. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology 949-962.
  • Dağ, S. 2007. Landslide Susceptibility Analysis of Çayeli (Rize) and its Surranding by Statistical Methods. Doktorate Thesis, KTU, Trabzon, (unpublished).
  • Dağ, S., Bulut, F. 2012. Preparation of GIS-based landslide susceptibility maps: Çayeli (Rize, NE Türkey) Research Article. Journal of Geological Engineering 35-62.
  • Dai, F.C., Lee, C.F. 2001. Terrain-based mapping of landslide susceptibility using a geographical information system: A case study. Can Geotech J 911–923.
  • Dai, F.C., Lee, C.F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 213–228.
  • Dai, F.C., Lee, C.F. 2003. A spatiotemporal probabilistic modelling of storm induced shallow landsliding using aerial photographs and logistic regression. Earth Surf Process Landforms 527–545.
  • Dehnavi, A., Aghdam, I.N., Pradhan, B., Varzandeh,M.H.M. 2015. A new hybrid model using step- wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy ınference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 122–148.
  • Demir, G. 2016. Landslide susceptibility assessment of the 1 part of the North Anatolian Fault Zone (Turkey) by GIS-based frequency ratio and index of entropy models. Nat. Hazards Earth Syst Sci 327-340.
  • Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., Althuwaynee, O.F. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat. Hazards 135–165.
  • Ding, Q., Chen, W., Hong, H. 2017. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping, Geocarto International.URL:https://www.researchgate.net/publication/298425978_Application_of_frequency_ratio_weights_of_evidence_and_evidential_belief_function_models_in_landslide_susceptibility_mapping
  • Dou, J., Yamagishi, H., Xu, Y., Zhu, Z., Yunus, A.P. 2017. Characteristics of the torrential rainfall- ınduced shallow landslides by typhoon bilis, in July 2006, using remote sensing and GIS. In book:GISLandslidePublisher:SpringerJapan,URL:https://www.researchgate.net/publication/317041333_Characteristics_of_the_Torrential_RainfallInduced_Shallow_Landslides_by_Typhoon_Bilis_in_July_2006_ Using_Remote_Sensing_and_GIS
  • Dölek, İ., Avcı, V. 2016. Determination of areas with landslide susceptibility in Arguvan district (Malatya province) and its surrounding by multi- criteria decision analysis method (MDAM). The Journal of Academic Social Science 106-129.
  • Dragicevi, C.S., Lai, T., Balram, S. 2015. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International 114–125.
  • Duman, T.Y., Çan, T., Gökçeoğlu, C., Nefeslioğlu, H.A., Sönmez, H. 2006. Application of logistic regression for landslide susceptibility zoning of Çekmece Area (İstanbul, Turkey). Environmental Geology 241-256.
  • Eker, A.M., Dikmen, M., Cambazoğlu, S., Akgün, H. 2012. Application of artificial neural network and logistic regression methods to landslide susceptibility mapping and comparison of the results for the Ulus district, Bartın. Journal of the Faculty of Engineering and Architecture of Gazi University 163-173.
  • Elkadiri, R., Sultan, M., Youssef, A.M., Elbayoumi, T., Chase, R., Bulkhi, A.B., Al-Katheeri, M.M. 2014. A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4818–4835.
  • Ercanoğlu, M. 2003. Production of landslide susceptibility maps by fuzzy logic and statistical methods: Western Black Sea Region (Kumluca-Yenice). Doctora Thesis, Hacettepe University, Ankara, (unpublished).
  • Ercanoğlu, M. 2005. Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks. Natural Hazards Earth System Science 979–992.
  • Ercanoğlu, M., Gökceoğlu, C. 2002. Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology 720–730.
  • Ercanoğlu, M., Gökçeoğlu, C. 2004. Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology 229–250.
  • Ercanoğlu, M., Gökceoğlu, C., Van Asch, Th. W.J. 2004. Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards 1–23.
  • Ercanoğlu, M., Kasmer, O., Temiz, N. 2008. Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment 565–578.
  • Erener, A., Lacasse, S., 2007. Landslide Susceptibility Mapping Using GIS. TMMOB Chamber of Survey and Cadastre Engineers National Geographic Information Systems Congress, KTÜ, Trabzon.
  • Fenghuan, S., Peng, C., Jianqiang, Z., Lingzhi, X., 2010. Susceptibility assessment of landslides caused by the Wenchaun earthquake using a logistic regression model. Journal of Mountain Science 234-245.
  • Fernandez, T., Irigaray, C., El Hamdouni, R., Chacon, J. 2003. Methodology for landslide susceptibility mapping by means of a GIS. Application to the contraviesa area (Granada, Spain). Nat. Hazards 297–308.
  • Fernandez, T., Jimenez, J., Fernandez, P., El Hamdouni, R., Cardenal, F.J., Delgado, J., Irigaray, C., Chacon, J. 2008. Automatic detection of landslide features with remote sensing techniques in the Betic Cordilleras (Granada, Southern Spain). Int Soc Photogramme, 351-356.
  • Gallart, F., Clotet, N. 1988. Some aspects of the geomorphic processes triggered by an extreme rainfall event: The November 1982 flood in The Eastern Pyrenees. Catena 79–95. Gattinoni, P. 2009. Parametrical landslide modeling for the hydrogeological susceptibility assessment: from the Crati Valley to the Cavallerizzo landslide (Southern Italy). Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards 161-178.
  • Goetz, J.N., Brenning, A., Petschko, H., Leopold, P. 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput. Geosci. 1–11.
  • Gomez, H., Kavzoğlu, T. 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology 11–27.
  • Gorsevski, P.V., Jankowski, P. 2008. Discerning landslide susceptibility using rough sets. Computers, Environment and Urban Systems 53-65.
  • Gorsevski, P.V., Donevska, K.R., Mitrovski, C.D., Frizado, J.P. 2012. Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: A case study using ordered weighted average. Waste Management 287-296.
  • Gökçeoğlu, C., Ercanoğlu, M. 2001. Uncertainties on the parameters employed in preparation of landslide susceptibility maps. Bulletin of Earth Sciences Application and Research Centre of Hacettepe University, 189-206.
  • Gökçeoğlu, C., Sönmez, H., Nefeslioğlu, H.A., Duman, T.Y., Çan, T. 2005. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide–susceptibility map of its near vicinity. Engineering Geology 65–83.
  • Görcelioğlu, E. 2003. Flood and avalanche control. Istanbul University Publication Faculty of Forestry Publication, 473-490.
  • Görüm, T. 2006. Landslide susceptibility analysis with geographic information systems and statistical methods: Melen Gorge and near vicinty. İstanbul University, Master Thesis, Istanbul (unpublished).
  • Gritzner, M.L., Marcus, W.A., Aspinall, R., Custer, S.G. 2001. Assessing landslide potential using GIS, soil wetness modelling and topographic attributes. Payette River, Idaho, Geomorphology 149-165.
  • Gruber, S., Haeberli, W. 2007. Permafrost in steep bedrock slopes and its temperature-related destabilization following climate change. J Geophys Res 112- 130, URL: https://agupubs.onlinelibrary.wiley. com/doi/10.1029/2006JF000547
  • Hasekioğulları, G.D. 2011. Assessment of parameter effects in producing landslide susceptibility maps. Master Thesis, Hacettepe University, Ankara (unpublished). He, S., Li, D., Wu, Luo, Y. 2011. Study on the rainfall and aftershock threshold for debris flow of post- earthquake. J. Mountain Sci 750–756.
  • Hong, H., Chen, W., Xu, C., Youssef, A.M., Pradhan, B., Tien, Bui, D. 2017a. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int 139–154.
  • Hong, H., Ilia, I., Tsangaratos, P., Chen, W., Xu, C. 2017b. A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 1–16.
  • Ilia, I., Tsangaratos, P. 2016. Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 379–397.
  • Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J., Sattarian, A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 909–926.
  • Jaafari, A., Najafi, A., Rezaeian, J., Sattarian, A. 2015a. Modeling erosion and sediment delivery from unpaved roads in the north mountainous forest of Iran. International Journal on Geomathematics 343–350.
  • Jaafari, A., Najafi, A., Rezaeian, J., Sattarian, A., Ghajar, I. 2015b. Planning road networks in landslide-prone areas: A case study from the northern forests of Iran. Land Use Policy 198–208.
  • Jebur, M.N., Pradhan, B., Tehrany, M.S. 2015. Manifestation of LiDAR derived parameters in spatial prediction of landslides using a novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J Sel Top Appl Earth Obs Remote Sens 674-689.
  • Jimenez-Peralvarez, J.D., Irigaray, C., El Hamdouni, R., Chacon, J. 2009. uilding models for automatic landslide-susceptibility analysis, mapping and validation in ArcGIS. Nat Hazards, 571 – 590.
  • Juang, C.H., Lee, D.H., Sheu, C. 1992. Mapping slope failure potential using fuzzy sets. Journal of Geotechnical Engineering ASCE 475–494.
  • Kamp, U., Growley, B.J., Khattak, G.A., Owen, L.A. 2008. GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 631–642.
  • Kavzoğlu, T., Çölkesen, İ. 2010. Classification of Satellite Images Using Decision Trees: Kocaeli Case. Electronic Journal of Map Technologies 36-45.
  • Kavzoğlu, T., Çölkesen, İ., Şahin, E.K. 2012. Investigation of the Effects of Factors Used in Production of Landslide Susceptibility Maps: A Case Study in Düzköy. IV. Remote Sensing and Geographic Information Systems Symposium (UZAL-GIS),Oct., 2012, Zonguldak.
  • Kavzoğlu, T., Şahin, E.K., Çölkesen, İ. 2014. Factor Selection based on Chi-Square Test in Landslide Sensitivity Analysis. V. Remote Sensing and Geographical Information Systems Symposium (UZAL-GIS), 14-17 Oct.,2014, İstanbul.
  • Kornejady, A., Heidari, K., Nakhavali, M. 2015. Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran Environ Resour Res 85–109.
  • Kornejady, A., Ownegh, M., Bahremand, A. 2017a. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 144–162.
  • Kornejady, A., Ownegh, M., Rahmati, O., Bahremand, A. 2017b. Landslide susceptibility assessment using three bivariate models considering the new topo- hydrological factor: HAND. Geocarto Int 1155– 1185.
  • Koukis, G., Ziourkas, C. 1991. Slope Instability Phenomena in Greece: A Statistical Analysis. Bulletin of International Association of Engineering Geologists 47-60.
  • Kouli, M., Loupasakis, C., Soupios, P., Rozos, D., Vallianatos, F. 2014. Landslide susceptibility mapping by comparing the WLC and WofE mutli- criteria methods in the West Crete Island, Greece. Environ Earth Sci.
  • Lan, H.X., Zhou, C.H., Wang, L.J., Zhang, H.Y., Li, R.H., 2004. Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China Eng Geol 109–128.
  • Lee, S., Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 33–41.
  • Lee, S., Choi, J., Min, K. 2002. Landslide susceptibility analysis and verication using the bayesian probability model. Enviromental Geology 120- 131.
  • Leonardi, G., Palamaraa, R., Ciriannia, F. 2016. Landslide Susceptibility mapping using a fuzzy approach procedia engineering, World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium, WMCAUS, 380–387.
  • Liu, C., Li, W., Wu, H. 2013. Susceptibility evaluation and mapping of China’s landslides based on multi- source data. Natural Hazards 1477–1495.
  • Liu, S., Wu, Y. 2016. Landslide susceptibility mapping in the Gangu County, China using artificial neural network and Gis. Bund 7614-7628.
  • Mashari, S., Solaimani, K., Omidvar, E. 2012. Landslide susceptibility mapping using multiple regression and gıs tools in Tajan Basin, north of Iran.URL:https://www.researchgate. net/publication/311921212_Landslide_ Susceptibility_Mapping_ Using_Multiple_ Regression_and_GIS_Tools_in_Tajan_Basin_ North_of_Iran
  • Mazman, T. 2005. Landslide susceptibility assessment in Kumluca (SE Bartın) watershed by geoeraphic information systems and statistical analysis methods. Master Thesis, Çukurova University, Adana (unpublished).
  • Meng, Q., Miao, F., Zhen, J., Wang, X., Wang, A., Peng, Y., Fan, Q. 2015. GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: A case study from Wolong Giant Panda Natural Reserve, China Bull Eng Geol Environ.
  • Meng, X., Pei, X., Liu, Q., Zhang, X., Hu, Y. 2016. GIS-based environmental assessment from three aspects of geology, ecology and society along the road from Dujiangyan to Wenchuan, Mt Res 110–120.
  • Mohammady, M., Pourghasemi, H.R., Pradhan, B. 2012. Landslide susceptibility mapping at Golestan Province Iran: A comparison between frequency ratio, Dempster-Shafer, andweights of evidence models. J Asian Earth Sci 221-230.
  • Moore, I.D., Grayson, R.B., Ladson, A.R. 1991. Digital terrain modelling: A review of hydrological, geomorphological and biological applications. Hydrological Processes 23-30.
  • Moradi, S., Rezaei, M. 2014. A GIS-based comparative study of the analytic hierarchy process, bivariate statistics and frequency ratio methods for landslide susceptibility mapping in part of the Tehran metropolis, Iran J Geope 45-61.
  • Myronidis, D., Papageorgiou, C., Theophanous, S. 2016. Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat. Hazards 245–263.
  • Nagarajan, R., Roy, A., Vinod, Kumar, R., Mukherjee, A., Khire, M.V. 2000. Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bulletin of Engineering Geology and the Environment 275–287.
  • Nefeslioğlu, H., Gökceoğlu, C., Sönmez, H. 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology 171- 191. Nourani, V., Pradhan, B., Ghaffari, H., Sharifi, S.S. 2014. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression and artificial neural network models. Nat Hazards 523–547.
  • Ochoa, G. 1978. La influencia de la altitud sobre algunas propiedades fisico-quı´micas de los suelos de los Andes venezolanos. Revista Geograifica 56–72.
  • Oh, H.J., Pradhan, B. 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers&Geosciences 1264-1276.
  • Opiso, E.M., Puno, G.R., Alburo, J.L.P., Detalla, A.L. 2016. Landslide susceptibility mapping using GIS and FR method along the Cagayan de Oro-Bukidnon- Davao City route corridor, Philippines. KSCE Journal of Civil Engineering 2506–2512.
  • Özdemir, A. 2009. Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 1675–1686.
  • Özdemir, A., Altural, T. 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 180–197.
  • Özşahin, E. 2015. Landslide susceptibility analysis by geographical information systems: the case of Ganos Mount (Tekirdağ). Electronic Journal of Map Technologies 47-63.
  • Özşahin, E., Kaymaz, Ç.K. 2013. Landslide susceptibility analysis of camili (Macahel) Biosphere Reserve Area (Artvin, NE Turkey). Turkish Studies - International Periodical for The Languages, Literature And History Of Turkish Or Turkic, 471-493.
  • Pachauri, A.K., Pant, M. 1992. Landslide hazard mapping based on geological attributes. Engineering Geology 81-100.
  • Pachauri, A.K., Gupta, P.V., Chander, R. 1998. Landslide zoning in a part of the Garhwal Himalayas. Env Geol 325-334.
  • Padrones, J. T., Ramos, N.T., Dimalanta, C.B., Queaño, K.L., Faustino-Eslava, D.V., Yumul, G.P. Jr., Watanabe, K. 2017. Landslide Susceptibility mapping in a geologically complex terrane: A case study from northwest Mindoro, Philippines Manila. Journal of Science 25–44.
  • Park, S., Jeon, S., Choi, C. 2010. Mapping urban growth probability in South Korea: Comparison of frequency ratio, analytic hierarchy process, and logistic regression models and use of the environmental conservation value assessment. Landscape and Ecological Engineering.
  • Pawluszek, K., Borkowski, A. 2017. Impact of DEM- derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Natural Hazards 919– 952.
  • Peng, L., Niu, R., Huang, B., Wu, X., Zhao, Y., Ye, R. 2014.Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology 287–301.
  • Pham, B.T., Tien, Bui, D., Indra, P., Dholakia, M. 2015. Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency ratio method. Int J Eng Res Technol 338–344.
  • Pham, B.T., Bui, D.T., Dholakia, M., Prakash, I., Pham, H.V. 2016. A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall- induced landslides in a tropical cyclones area. Geotech Geol Eng 1–18.
  • Pham, B.T., Bui, D.T., Prakash, I., Dholakia, M.B. 2017. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 52–63.
  • Pourghasemi, H.R., Gökçeoğlu, C., Pradhan, B., Deylami, Moezzi, K. 2012a. Landslide susceptibility mapping using a spatial multicriteria evaluation model at Haraz Watershed Iran, In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements, Springer Berlin, 23–49.
  • Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C. 2012b. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed Iran. Nat Hazards URL:https://www.researchgate.net/ publication/230875072_Application_of_fuzzy_ logic_and_analytical_hierarchy_process_AHP_ to_landslide_susceptibility_mapping_at_Haraz_ watershed_Iran
  • Pourghasemi, H.R., Mohammady, M., Pradhan, B. 2012c. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97, 71–84.
  • Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C. 2012d. Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS. AEROTECH IV– 2012, Appl Mech Mater, 486–491. Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C., Mohammadi, M., Moradi, H.R. 2012e. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 2351–2365.
  • Pourghasemi, H.R., Moradi, H.R., Aghda, S.F. 2013a. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 749–779.
  • Pourghasemi, H.R., Pradhan, B., Gökçeoğlu, C., Mohammadi, M., Moradi, H.R., 2013b. Application of weights-of-evidence and certainty factor models and their comparison inlandslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 2351.
  • Pourghasemi, H.R., Beheshtirad, M. 2015. Assessment of a data–driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int 662–685.
  • Pourghasemi, H.R., Kerle, N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran province, Iran. Environmental Earth Sciences 185.
  • Pourghasemi, H.R., Rossi, M. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 609–633.
  • Pourtaghi, Z.S., Pourghasemi, H.R., Rossi, M. 2014. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth 1515–1533.
  • Pradhan, A.M.S., Kim, Y.T. 2014. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Natural Hazards 1189–1217.
  • Pradhan, A.M.S, Kim, Y.T. 2015. Application and comparison of shallow landslide susceptibility models in weathered granite soil under extreme rainfall events. Environ. Earth Sci 5761–5771.
  • Pradhan, A.M.S., Kim, Y.T. 2017. Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon, Seoul, Korea. Bull Eng Geol Environ 1263–1279.
  • Pradhan B, Abokharima, M.H., Jebur, M.N., Tehrany, M.S. 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using, the evidential belief function model in GIS. Nat Hazards 112. Raja, N. B., Çiçek, I., Türkoğlu, N., Aydın, O., Kawasaki, A. 2017. Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Natural Hazards 1323-1346.
  • Ramesh, V., Anbazhagan, S. 2015. Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci 8009–8021.
  • Ray, R.L., Jacobs, J.M. 2007. Relationships among remotely sensed soil moisture, precipitation and landslide events. Natural Hazards 211–222.
  • Rozos, D.E.L., Skias, P.S., Tsangaratos, P. 2008. An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa County. Landslides 261-270.
  • Rozos, D., Bathrellos, G.D., Skilodimou, H.D. 2010. Landslide susceptibility mapping of the northeastern part of Achaia Prefecture using Analytical Hierarchical Process and GIS techniques. Bulletin of the geological society of Greece, Proceeding of the12th International Congress, Patras may, XLIII, 1637-1646.
  • Rozos, D., Bathrellos, G.D., Skilodimou, H.D. 2011. Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: A case study from the Eastern Achaia County of Peloponnesus Greece. Environ Earth Sci 49–63.
  • Sabatakakis, N., Koukis, G., Vassiliades, E., Lainas, S. 2013. Landslide susceptibility zonation in Greece. Natural Hazards 523–543.
  • Saadatkhahi, N., Kassimi, A., Lee, M. L. 2014. Qualitative and quantitative landslide susceptibility assessments in Hulu Kelang area, Malaysia. EJGE, 545-563. URL: http://www.ejge.com/2014/ Ppr2014.047mar.pdf
  • Sadr, M. P., Abbas, M., Bashir, S.S. 2014. Landslide susceptibility mapping of Komroud sub-basin using fuzzy logic approach. Geodyn Res Int Bull 14–27.
  • Sancar, C. 2000. Economy-ecology sensitive planning model and GIS for determination of urban development areas and planning. Phd Thesis, KTU, Trabzon (unpublished).
  • Schicker, R., Moon, V. 2012. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 40-57.
  • Sezer, E.A., Pradhan, B., Gökçeoğlu, C. 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications 8208-8219. Shrestha, S., Kang, T.S., Suwal, M. 2017. An Ensemble model for co-seismic landslide susceptibility using gıs and 29 random forest method. ISPRS International Journal of Geo-Information 365.
  • Simon, N. de Róiste M., Crozier, M., Rafek, A.G. 2017. Representing Landslides as Polygon (Areal) or Points? How Different Data Types Influence the Accuracy of Landslide Susceptibility Maps, Sains Malaysiana, 27-34.
  • Sujatha, E.R., Kumaravel, P., Rajamanickam, G.V. 2014. Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bull Engg Geol Environ 147–161.
  • Süzen, M.L., Doyuran, V. 2004a. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology 303-321.
  • Süzen, M.L., Doyuran, V. 2004b. A comparison of the GIS based landslide susceptibility assessment methods: Multivariate Versus Bivariate. Environmental Geology 665-679.
  • Süzen, M.L., Kaya, B.Ş. 2011. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth 1–18.
  • Tangestani, M.H. 2003. Landslide susceptibility mapping using the fuzzy gamma operation in a GIS, Kakan catchment area, Iran. Map India Conference. URL: file:/// C:/ Users/ Dekan/ Downloads/ Landslide susceptibility_mapping_using thefuzzyg.pdf
  • Tangestani, M. H. 2004. Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Australian Journal of Earth Sciences 439–450.
  • Tanoli, J. I., Ningsheng, C., Regmi, A. D., Jun, L. 2017. Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arabian Journal of Geosciences 10-13.
  • Tazik, E., Jahantab, Z., Bakhtiari, M., Rezae, A., Alavipanah, S. K. 2014. Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain Basin. International Conference on Geospatial Information Research (GI Research) 15-17 November 2014, Tehran, Iran.
  • Tsangaratos, P., Ilia, I. 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 164–179. Umar, Z., Pradhan, B., Ahmad, A., Neamah, Jebur, M., Shafapour, Tehrany, M. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera province, Indonesia. Catena 124–135.
  • Vivas, L. 1992. Los Andes Venezolanos, Academia Nacional de la Historia, Caracas.Wan, S., Lei, T. C., Chou, T.Y. 2012. Alandslide expert system: image classification through integration of data mining approaches for multi-category analysis. International Journal of Geographical Information Science 747-770.
  • Wang, H. Q., He, J., Liu, Y., Sun, S. 2016. Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 422.
  • Wang, L., Guo, M., Sawada, K., Lin, J., Zhang, J. 2015. Landslide susceptibility mapping in Mizunami city, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Geomorphology 271–282.
  • Wang, Y., Zhao, B., Li, J. 2017. Mechanism of the catastrophic June 2017 landslide at Xinmo Village, Songping River, Sichuan Province, China. Landslides 333–345.
  • Wu, Y., Ke, Y. 2016. Landslide susceptibility zonation using GIS and evidential belief function model. Arabian Journal of Geosciences 697.
  • Wu, Y., Li, W., Liu, P., Bai, H., Wang, Q., He, J., Liu, Y., Sun, S. 2016. Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 422.
  • Xu, C., Xu, X.W. 2012. Spatial prediction models for seismic landslides based on support vector machine and varied kernel functions: a case study of the 14 April 2010 Yushu earthquake in China. Chin J Geophys 666–679.
  • Xu, C., Xu, X.W. 2013. Controlling parameter analyses and hazard mapping for earthquake triggered- landslides: an example from a square region in Beichuan County, Sichuan Province, China. Arab J Geosci 3827–3839.
  • Xu, C., Xu, X., Shen, L., Yao, Q., Tan, X., Kang, W., Ma, S., Wu, X., Cai, J., Gao, M., Li, K. 2016a. Optimized volume models of earthquake-triggered landslides. Scientific Reports 6, 29797.
  • Xu, C., Xu, X., Tian, Y., Shen, L., Yao, Q., Huang, X., Ma, J., Chen, X., Ma, S. 2016b. Two comparable earthquakes produced greatly different coseismic landslides: The 2015 Gorkha, Nepal and 2008 Wenchuan, China events. Journal of Earth Science 1008-1015.
  • Yalçın, A., Reis, S., Aydınoğlu, A.C., Yomralıoğlu, T. 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 274-287.
  • Yang, Z.H., Lan, H.X., Gao, X., Li, L.P., Meng, Y.S.,Wu, Y.M. 2015. Urgent landslide susceptibility assessment in the 2013 Lushan earthquake- impacted area, Sichuan Province, China. Nat Hazards 2467–2487.
  • Yao, X., Tham, L.G., Dai, F.C. 2008. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China. Geomorphology 572-582.
  • Yılmaz, Ç., Topal, T., Süzen, M.L. 2012. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Sciences 2161-2178.
  • Yılmaz, I. 2009a. A case study from Koyulhisar (Sivas- Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 297–306.
  • Yılmaz, I. 2009b. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Comput Geosci 1125–1138.
  • Yılmaz, I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 821–836.
  • Yılmaz, I., Keskin, İ. 2009. GIS based statistical and physical approaches to landslide susceptibility mapping (Şebinkarahisar, Turkey). Bulletin of Engineering Geology and the Environment 459-471.
  • Youssef, A.M. 2015. Landslide susceptibility delineation in the Ar-Rayth Area, Jizan, Kingdom of Saudi Arabia, by using analytical hierarchy process, frequency ratio,and logistic regression models. Environ Earth Sci 8499–8518.
  • Youssef, A.M., Al-Kathery, M., Pradhan, B. 2014a. Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 113–134.
  • Youssef, A. M., Al-Kathery, M., Pradhan, B., Elsahly, T. 2014b. Debris flow impact assessment along the Al-Raith Road, Kingdom of Saudi Arabia, using remote sensing data and field investigations.Geomat Nat Hazards Risk 7-20.
  • Youssef, A. M., Pradhan, B., Jebur, M. N., El-Harbi, H. M., 2014c. Landslide susceptibility mappin gusing ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci 73-80.
  • Youssef, A.M., Al-Kathery, M., Pradhan, B. 2015. Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 113–134.
  • Yüksel, N. 2007. Usage of statistical techniques and artificial neural networks in producing landslide susceptibility maps based on geographical information systems: Kumluca-Ulus (Bartın) region. Phd Thesis, Hacettepe University, Ankara (unpublished).
  • Zare, M., Jouri, M.H., Salarian, T., Askarizadeh, D., Miarrostami, S. 2014. Comparing of bivariate statistic, AHP and combination methods to predict the landslide hazard in northern aspect of Alborz Mt. (Iran). Intl J Agri Crop Sci 543-554.
  • Zhang, J., Yin, K., Wang, J., Liu, L., Huang, F. 2016a. Evaluation of landslide susceptibility for Wanzhou district of three Gorges reservoir. Chinese Journal of Rock Mechanics and Engineering 35.
  • Zhang, J. Q., Liu, R. K., Deng, W., Khanal, N. R., Gurung,D. R., Sri, Ramachandra, Murthy, M., Wahid, S. 2016b. Characteristics of landslide in Koshi River Basin, Central Himalaya. Journal of Mountain Science 1711–1722.
  • Zhang, K., Wu, X., Niu, R., Yang, K., Zhao, L. 2017. The Assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir Area, China. Environ Earth Sci 405. Zhang, M.S., Dong, Y., Sun, P.P. 2012. Impact of reservoir impoundment-caused groundwater level changes on regional slope stability: a case study in the Loess Plateau of Western China. Environ Earth Sci 1715–1725.
  • Zhao, W., Li, A., Deng, W. 2014. Surface energy fluxes estimation over the South Asia subcontinent through assimilating MODIS/TERRA satellite data with In Situ observations and GLDAS product by SEBS model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3704–3712.
  • Zhu, H. H., Shi, B., Yan, J. F., Zhang, J., Zhang, C. C., Wang, B. J. 2014. Fiber Bragg grating-based performance monitoring of a slope model subjected to seepage. Smart Mater Struct 23, 095027. URL: https://iopscience.iop.org/ article/10.1088/0964-1726/23/9/095027
  • Zhuang, J., Peng, C., Wang, G., Chen, X., Iqbal, J., Guo,X. 2015. Rainfall thresholds for the occurrence of debris flows in the Jiangjia Gully, Yunnan Province, China. Engineering Geology, 195. URL:https:// www.researchgate.net/publication/281746840_Rainfall_thresholds_for_the_occurrence_of_debris_flows_in_the_Jiangjia_Gully_Yunnan_ Province_China
  • Zolotraev, W.H. 1976. Present day problems in the engineering geological investigation of landslides, falls and mudflows in mountainous folded regions. In: Hutchinson JN (ed) Geological factors and mechanism involved in the development of landslides, falls and mudflows. UNESCO, Paris, 5-34.
Toplam 200 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Seda Çellek 0000-0001-9675-5691

Yayımlanma Tarihi 15 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 162 Sayı: 162

Kaynak Göster

APA Çellek, S. (2020). Morphological parameters causing landslides: A case study of elevation. Bulletin of the Mineral Research and Exploration, 162(162), 197-224. https://doi.org/10.19111/bulletinofmre.649758
AMA Çellek S. Morphological parameters causing landslides: A case study of elevation. Bull.Min.Res.Exp. Ağustos 2020;162(162):197-224. doi:10.19111/bulletinofmre.649758
Chicago Çellek, Seda. “Morphological Parameters Causing Landslides: A Case Study of Elevation”. Bulletin of the Mineral Research and Exploration 162, sy. 162 (Ağustos 2020): 197-224. https://doi.org/10.19111/bulletinofmre.649758.
EndNote Çellek S (01 Ağustos 2020) Morphological parameters causing landslides: A case study of elevation. Bulletin of the Mineral Research and Exploration 162 162 197–224.
IEEE S. Çellek, “Morphological parameters causing landslides: A case study of elevation”, Bull.Min.Res.Exp., c. 162, sy. 162, ss. 197–224, 2020, doi: 10.19111/bulletinofmre.649758.
ISNAD Çellek, Seda. “Morphological Parameters Causing Landslides: A Case Study of Elevation”. Bulletin of the Mineral Research and Exploration 162/162 (Ağustos 2020), 197-224. https://doi.org/10.19111/bulletinofmre.649758.
JAMA Çellek S. Morphological parameters causing landslides: A case study of elevation. Bull.Min.Res.Exp. 2020;162:197–224.
MLA Çellek, Seda. “Morphological Parameters Causing Landslides: A Case Study of Elevation”. Bulletin of the Mineral Research and Exploration, c. 162, sy. 162, 2020, ss. 197-24, doi:10.19111/bulletinofmre.649758.
Vancouver Çellek S. Morphological parameters causing landslides: A case study of elevation. Bull.Min.Res.Exp. 2020;162(162):197-224.

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