EFFECT OF THE CURVATURE PARAMETER AND İTS CLASSİFİCATİON ON LANDSLİDES

Yıl 2024, , 49 - 63, 25.03.2024

Seda Çellek

https://doi.org/10.21923/jesd.1391818

Öz

The first question that generally comes to mind about the curvature parameter is whether this parameter is suitable for the study area. This question uses every parameter to be asked, but some effects that are implemented incorrectly, such as curvilinearity, raise question marks. As a result of technical errors and conceptual confusion regarding the parameter, the landslide area defined as concave by one researcher may be defined as convex by another researcher. For this reason, some researchers state that they contradict the literature and produce results contrary to their expectations. Due to such negativities, there is no consensus in the literature regarding curvilinearity parameters. This determination was used for 64 areas selected for curvature parameters in three different classes and the prices of their changes in total. By examining the maximum and minimum distributions in the landslide area, it was investigated what kind of change it caused in concave, convex and flat areas depending on the terrain. As a result of the analysis, it was revealed that class intervals that could not be determined correctly resulted in cracks in the landslide capacity proportional distributions. Thus, the study achieves the main goal that will facilitate the use of the curvature parameter.

Anahtar Kelimeler

Landslide Susceptibility, Curvature, Concave, Convex, Parameter

Proje Numarası

MMF.A4.18.017

EĞRİSELİK PARAMETRESİ VE SINIFLANDIRILMASININ HEYELANLARA ETKİSİ

Öz

Eğrisellik parametresini seçerken akla gelen ilk soru, bu parametrenin çalışma alanına uygun olup olmadığıdır. Genellikle bu soru kullanılan her parametre için sorulur, ancak eğrisellik gibi uygulama hatası yapılan bazı parametreler soru işaretlerine neden olur. Parametre ile ilgili yapılan teknik hatalar ve kavram karmaşası sonucu, bir araştırmacı tarafından içbükey olarak tanımlanan heyelanlı alan, diğer bir araştırmacı tarafından dış bükey olarak tanımlanabilmektedir. Bu nedenle bazı çalışmalarda, araştırmacılar literatürle çelişmekte ve kendi alanlarının beklentilerinin aksine sonuçlar verdiğini belirtmektedir. Bu gibi olumsuzlular nedeniyle, eğrisellik parametresi konusunda literatürde fikir birliği sağlanamamaktadır. Yapılan bu çalışmada seçilen 64 alan için üç farklı sınıftaki eğrisellik parametresi kullanılmış ve bunların toplamdaki değişimleri incelenmiştir. Heyelanlı alandaki maksimum ve minimum dağılımlar incelenerek bunun araziye göre içbükey, dışbükey ve düz alanlarda nasıl bir değişime neden olduğu araştırılmıştır. Analizler sonucunda, doğru belirlenmeyen sınıf aralıklarının, heyelan alanlarının oransal dağılımlarında farklılıklar çıkarttığı ortaya çıkmıştır. Böylece çalışma ana hedefine ulaşarak, eğrisellik parametresinin kullanımını kolaylaştıracak ip uçları vermiştir.

Anahtar Kelimeler

Heyelan Duyarlılık, Eğrisellik, Konkav, Konveks, Parametre

Destekleyen Kurum

Kırşehir Ahi Evran Üniversitesi BAP Birimi

Proje Numarası

MMF.A4.18.017

Teşekkür

Kırşehir Ahi Evran Üniversitesi BAP Birimi'ne teşekkür ederim

Kaynakça

• Abe, K., Ziemer, R. 1991. Effect of tree roots on shallow-seated landslide. XIV IUFRO World Congress, Montreal, Quebec, Canada.
• Afungang, R.N., Bateira, C.V. 2016. Temporal probability analysis of landslides triggered by intense rainfall in the Bamenda Mountain Region, Cameroon. Environmental Earth Science, 75:1032.
• Ahmed, M.F., Rogers, J.D., Ismail, E.H. 2014. A regional level preliminary landslide susceptibility study of the upper Indus river basin. European Journal of Remote Sensing 47:1, 343-373. https://doi.org 10.5721/EuJRS20144721
• Akıncı, H., Doğan, S., Kılıçoğlu, C., Keçeci, S.B. 2010. Production of landslide susceptibility map of Samsun province center. Electronic Journal of Map Technologies 2:3, 13–27 (in Turkish).
• Aksoy, B. 2010. Determination of landslide locations by object-based image analyses: Western Black Sea region. Master Thesis, Hacettepe University Turkey (in Turkish).
• Altural, T. 2012. Landslide susceptibility assessment of vicinity of Akşehir (Konya) using geographic information systems. Selçuk University Master Thesis, Turkey (in Turkish).
• Altürk, G. 2019. Mapping landslide susceptibility by using machine learning and statistical methods in geographical information systems environment: Rize Taşlidere basin sample. Master Thesis Gebze Teknik University Turkey (in Turkish).
• Anis, Z., Gallala, W., Vakhshoori, V., Smida, H., Gaied, M.E. 2019. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. Open Geosciences 11:1, 708–726. https://doi.org/10.1515/geo-2019-0056.
• Aras, Ö. 2021. Natural disaster risk analysis inHavza (Samsun) depression. Doctorate Thesis Ondokuz Mayıs University, Turkey (in Turkish).
• 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.
• Aydoğan, E. 2019. Landslide susceptibility analysis of upper Karasu watershed located between Aziziye-Aşkale part. Master Thesis Gümüşhane University, Turkey (in Turkish).
• Başara, A.C., 2021. production of landslide susceptibility maps by statistical methods and investigation of spatial susceptibility. Doctorate Thesis Ondokuz Mayıs University, Turkey (in Turkish).
• Biber, T.C. 2019. Comparısıon of the methods used to produce landslide susceptibility maps: instance of Şebinkarahisar district. Doctorate Thesis Karadeniz Teknik University, Turkey (in Turkish).
• Booth, A.M., Roering, J.J.and Perron, J.T., 2009. Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology, 109, 132-147.
• Bray, J.W. 1977. Rock slope engineering The Institution of Mining and Metal lugy. Stephen Austin and Sons Hertford, 402.
• 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.
• 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.
• Çelebi, S. 2021. The assesment of integrated natural disaster susceptibility for Artvin province (Central district). Doctorate Thesis Karadeniz Teknik University Turkey (in Turkish).
• Çellek, S. 2013. Landslide susceptibility analysis of Sinop-Gerze region. Doctorate Thesis Karadeniz Teknik University, Turkey (in Turkish).
• Çil, E. 2009. Gis based landslide hazard assessment of the Erdemli (Mersin) region. Çukurova University, Turkey (in Turkish).
• Dahal, R.K. 2014. Regionalscale landslide activity and landslide susceptibility zonation in the Nepal Himalaya. Environmental Earth Sciences 71:12, 5145-5164.
• Dai, F.C., Lee, C.F. 2002a. Landslides on natural terrain physical characteristics and susceptibility mapping in Hong Kong. Mountain Research and Development 22:1, 40-47.
• Dai, F.C., Lee, C.F. 2002b. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42, 213-228.
• Eker, R. 2013. Mapping landslide susceptibility using geographical informatıon systems and its evaluation for forest roads in the Yıgılca forest directorate. Master Thesis, Duzce University Turkey (in Turkish).
• El-fengour. A., Riskam, C.B., El motaki, H., Garcia, J.H. 2020. Landslide susceptibility assessment based on information value model in Amzaz Watershed in Northern Morocco. Physis Terrae 2:2, 3-19.
• Elmacı H, Tekin S, Ünsal N (2017) Geographical informatıon systems based using logistic regression landslide susceptibility assessment of the çubuk-kalecik (Ankara) between Şabanözü (Çankırı) region. The Bulletin of Mineral Research and Exploration. 155:155, 175–186. https://doi.org/10.19111/bulletinofmre.306692
• Ermini, L., Catani, F., Casagli, N. 2005. Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66, 327–343.
• Freer, J., McDonnell, J.J., Beven, K.J., Peters, N.E., Burns, D.A., Hooper, R.P., Aulenbach, B., Kendall, C. 2002. The Role of bedrock topography on subsurface storm flow. Water Resources Research 38, 1269. http://dx.doi.org/10.1029/2001WR000872
• Ghobadi, M.H., Nouri, M., Saedi, B., Jalali, S.H., Pirouzinajad, N. 2017. The Performance evaluation of ınformation value, density area, LNRF, and frequency ratio methods for landslide zonation at Miandarband Area, Kermanshah province, Iran. Arabian Journal of Geosciences, 10: 430.
• https://doi.org/10.1007/s12517-017-3202-y
• Gökçeoğlu, C., Ercanoğlu, M. 2001. Heyelan duyarlılık haritalarının hazırlanmasında kullanılan parametrelere ilişkin belirsizlikler. Hacettepe Yerbilimleri Dergisi 23:189-206.
• Görüm, T. 2006. Landslide susceptibility analysis with geographic information systems and statistical methods: Melen Gorge and near vicinty. Master Thesis İstanbul University, Turkey (in Turkish).
• Günini, N.Ü. 2019. Assessment of landslide susceptibility in Van province using logistic regression analysis and frequency ratio method. Master Thesis Ondokuz Mayıs University, Turkey (in Turkish).
• Gibson, M., Forster, A., Entwisle, A.D., Wildman, G. 2008. GIS-based landslide assessment, Glascow, Scotland.
• Hasekioğulları, G.C. 2010. Assessment of parameter effects in producing landslide susceptibility maps. Master Thesis Hacettepe University, Turkey (in Turkish).
• Hong, H., Ilia, I., Tsangaratos, P., Chen, W., Xu, C. 2017. A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 290:1-16. doi.org/10.1016/ j.geomorph.2017.04.002
• Jakob, M. 2000. The Impacts of logging on landslide activity at Clayoquot Soung, British Columbia. Catena 38:279-300.
• Kayhan, H. 2021. Landslide susceptibility mapping of the Izmir metropolitan area. Master Thesis Dokuz Eylül University, Turkey (in Turkish).
• Kakembo, V. 1997. A reconstruction of the history of land degradation in relation to land use change and land tenure in peddie district, former ciskei. Master thesis Rhodes University Güney Afrika.
• Karaman, M.O. 2019 Landslide suspectibility mapping of Karaburun Peninsula with geographic information systems. Master Thesis Eskişehir Teknik University, Turkey (in Turkish).
• Kayastha, P. 2015. Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: A case study from Garuwa sub-basin, East Nepal. Arabian Journal of Geoscience 8:10, 8601-8613. doi.org/10.1007/s12517-015-1831-6
• Kayhan, H. 2021. Landslide susceptibility mapping of the Izmir metropolitan area. Master Thesis Dokuz Eylül University, Turkey (in Turkish).
• Klose, M., Highland, L., Damm, B., Terhorst, B. 2014. Estimation of Direct Landslide Costs in Industrialized Countries: Challenges, Concepts, and Case Study. Landslide Science for a Safer Geoenvironment pp 661–667 https://doi.org/10.1007/978-3-319-05050-8_10
• Komac, M. 2006. A Landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine Slovenia. Geological Survey of Slovenia 74:17-28.
• Kornejady, A., Ownegh, M., Bahremand, A. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 144–162.
• Lee, S., Min, K. 2001. Statistical Analysis of landslide susceptibility at Yongin, Korea Environ. Geol. 40:1095–1113.
• Lee, S., Ryu, J.H., Lee, M.J., Won, J.S. 2003b. Use of an artificial neural networkfor analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology 44:820–833.
• Lee, S., Ryu, J.H., Min, K., Won, J.S. 2003a. Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms 28:1361– 1376.
• Li, B., Wang, N., Chen, J. 2021. GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China. Hindawi Advances in Civil Engineering, 14. https://doi.org/10.1155/2021/4758062
• Mazman, T. 2005. Landslide susceptibility assessment in Kumluca (se Bartın) watershed by geoeraphic information systems and statistical analysis methods. Master Thesis Çukurova University, Turkey (in Turkish).
• Meinhardt, M., Fink, M., Tünschel, H. 2015. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80-97.
• Moradi, S., Rezaei, M.A. 2014. 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 Journal of Geopersia, 4:1, 45-61. doi:10.22059/jgeope.2014.51191
• Ohlmacher, G.C. 2007. Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology 91:117–134.
• 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.
• Paudel, U., Oguchi, T., Hayakawa, Y. 2016. Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest. International Journal of Geosciences 7:726-74. http://dx.doi.org/10.4236/ijg.2016.75056
• Pimiento, E. 2009. Shallow Landslide Susceptibility Modelling and Validation. Master Thesis, Lund University, Sweden.
• Posner, A.J., Georgakakos, K.P. 2015. Normalized landslide ındex method for susceptibility map development in El Salvador. Natural Hazards, 79:1825–1845.
• 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.
• Şahin, E.K. 2017. Determination of effective factors using feature selection algorithms and production of landslide susceptibility maps. Doctorate Thesis İstanbu Teknik University, Turkey (in Turkish).
• Tarolli, P., Tarboton, D.G. 2006. A new method for determination of most likely landslide ınitiation points and the evaluation of digital terrain model scale in terrain stability mapping. Hydrology and Earth System Sciences, 10, 663-677. http://dx.doi.org/10.5194/hess-10-663-2006
• Taşkanat, T. 2020. Production of landslide susceptibility maps using evolutionary algorithms. Doctorate Thesis Erciyes University, Turkey (in Turkish).
• url-1Curvature (Spatial Analyst) https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/curvature.htm
• url-2Understanding curvature rasters https://www.esri.com/arcgis-blog/products/product/imagery/understanding-curvature-rasters/
• Wilson, J.P., Gallant, J.C. 2000. Digital terrain analysis, Chapter 1. Terrain analysis: Principles and applications. New York, 1-27.
• 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. Doktorate Thesis Hacettepe University, Turkey (in Turkish).
• Zezere, J.L., Garcia RAC, Oliveira SC, Reis E (2007) Probabilistic landslide risk analysis considering direct costs in the area North.