Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye

Year 2024, , 147 - 164, 28.07.2024

Fatih Kadı , Osman Salih Yılmaz

https://doi.org/10.26833/ijeg.1355615

Abstract

Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems.

Keywords

Frequency ratio, Modified information value, Landslide susceptibility map, Forest roads, Costpath analysis

References

• Şentürk, E., & Erener, A. (2017). Determination of temporary shelter areas in natural disasters by GIS: A case study, Gölcük/Turkey. International Journal of Engineering and Geosciences, 2(3), 84-90. https://doi.org/10.26833/ijeg.317314
• Kaya, H., & Gazioğlu, C. (2015). Real estate development at landslides. International Journal of Environment and Geoinformatics, 2(1), 62-71. https://doi.org/10.30897/ijegeo.302433
• Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards, 87, 145-164. https://doi.org/10.1007/s11069-017-2757-y
• EM-DAT. (2023). The International Disaster Database. Inventorying hazards & disasters worldwide since 1988. https://www.emdat.be
• Görüm, T., & Fidan, S. (2021). Spatiotemporal variations of fatal landslides in Turkey. Landslides, 18(5), 1691-1705. https://doi.org/10.1007/s10346-020-01580-7
• Dahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., & Nishino, K. (2008). GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54, 311-324. https://doi.org/10.1007/s00254-007-0818-3
• Chen, W., Li, W., Hou, E., Zhao, Z., Deng, N., Bai, H., & Wang, D. (2014). Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China. Arabian Journal of Geosciences, 7(11), 4499-4511. https://doi.org/10.1007/s12517-014-1369-z
• Ercanoglu, M., & Gokceoglu, C. (2004). Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75(3-4), 229-250. https://doi.org/10.1016/j.enggeo.2004.06.001
• Komac, M., & Ribičič, M. (2006). Landslide susceptibility map of Slovenia at scale 1: 250,000. Geologija, 49(2), 295-309. https://doi.org/10.5474/geologija.2006.022
• Petschko, H., Brenning, A., Bell, R., Goetz, J., & Glade, T. (2014). Assessing the quality of landslide susceptibility maps–case study Lower Austria. Natural Hazards and Earth System Sciences, 14(1), 95-118. https://doi.org/10.5194/nhess-14-95-2014
• Chawla, A., Chawla, S., Pasupuleti, S., Rao, A. C. S., Sarkar, K., & Dwivedi, R. (2018). Landslide susceptibility mapping in darjeeling Himalayas, India. Advances in Civil Engineering, 2018(1), 6416492. https://doi.org/10.1155/2018/6416492
• Silalahi, F. E. S., Pamela, Arifianti, Y., & Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6(1), 10. https://doi.org/10.1186/s40562-019-0140-4
• Ram, P., Gupta, V., Devi, M., & Vishwakarma, N. (2020). Landslide susceptibility mapping using bivariate statistical method for the hilly township of Mussoorie and its surrounding areas, Uttarakhand Himalaya. Journal of Earth System Science, 129, 1-18. https://doi.org/10.1007/s12040-020-01428-7
• Sangeeta, Maheshwari, B. K., & Kanungo, D. P. (2020). GIS-based pre-and post-earthquake landslide susceptibility zonation with reference to 1999 Chamoli earthquake. Journal of Earth System Science, 129, 1-20. https://doi.org/10.1007/s12040-019-1319-y
• Bahrami, Y., Hassani, H., & Maghsoudi, A. (2021). Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal, 86, 1797-1816. https://doi.org/10.1007/s10708-020-10162-y
• Kadi, F., Yildirim, F., & Saralioglu, E. (2021). Risk analysis of forest roads using landslide susceptibility maps and generation of the optimum forest road route: a case study in Macka, Turkey. Geocarto International, 36(14), 1612-1629. https://doi.org/10.1080/10106049.2019.1659424
• Roccati, A., Paliaga, G., Luino, F., Faccini, F., & Turconi, L. (2021). GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land, 10(2), 162. https://doi.org/10.3390/land10020162
• Kincal, C., & Kayhan, H. (2022). A combined method for preparation of landslide susceptibility map in Izmir (Türkiye). Applied Sciences, 12(18), 9029. https://doi.org/10.3390/app12189029
• Roy, P., Ghosal, K., & Paul, P. K. (2022). Landslide susceptibility mapping of Kalimpong in Eastern Himalayan Region using a Rprop ANN approach. Journal of Earth System Science, 131(2), 130. https://doi.org/10.1007/s12040-022-01877-2
• Sweta, K., Goswami, A., Nath, R. R., & Bahuguna, I. M. (2022). Performance assessment for three statistical models of landslide susceptibility zonation mapping: A case study for Dharamshala Region, Himachal Pradesh, India. Journal of Earth System Science, 131(3), 143. https://doi.org/10.1007/s12040-022-01881-6
• Khusulio, K., & Kumar, R. (2023). Feasibility assessment of multi-criteria decision making and quantitative landslide susceptibility methods: A case study of Mao-Maram Manipur. Journal of Earth System Science, 132(2), 56. https://doi.org/10.1007/s12040-023-02062-9
• Som, S. K., Ghosh, S., Dasgupta, S., Kumar, N. T., Hindayar, J. N., Mohan, M., ... & Bhattacharya, S. (2023). Utility of common variance of equally-weighted variables for GIS-based landslide susceptibility mapping at the eastern Himalaya. Journal of Earth System Science, 132(1), 16. https://doi.org/10.1007/s12040-022-02017-6
• Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. J. N. H. (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Sciences, 6(1), 115-131. https://doi.org/10.5194/nhess-6-115-2006
• Erener, A., Mutlu, A., & Düzgün, H. S. (2016). A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Engineering Geology, 203, 45-55. https://doi.org/10.1016/j.enggeo.2015.09.007
• Loche, M., Alvioli, M., Marchesini, I., Bakka, H., & Lombardo, L. (2022). Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory. Earth-Science Reviews, 232, 104125. https://doi.org/10.1016/j.earscirev.2022.104125
• Liu, S., Wang, L., Zhang, W., Sun, W., Fu, J., Xiao, T., & Dai, Z. (2023). A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir Area. Geoscience Frontiers, 14(5), 101621. https://doi.org/10.1016/j.gsf.2023.101621
• Luo, W., & Liu, C. C. (2018). Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides, 15, 465-474. https://doi.org/10.1007/s10346-017-0893-9
• Akinci, H., & Yavuz Ozalp, A. (2021). Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophysica, 69(3), 725-745. https://doi.org/10.1007/s11600-021-00577-7
• Pradhan, B. (2011). Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Sciences, 63(2), 329-349. https://doi.org/10.1007/s12665-010-0705-1
• Kumar, R., & Anbalagan, R. (2016). Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. Journal of the Geological Society of India, 87, 271-286. https://doi.org/10.1007/s12594-016-0395-8
• Fatemi Aghda, S. M., Bagheri, V., & Razifard, M. (2018). Landslide susceptibility mapping using fuzzy logic system and its influences on mainlines in lashgarak region, Tehran, Iran. Geotechnical and Geological Engineering, 36, 915-937. https://doi.org/10.1007/s10706-017-0365-y
• Mandal, S., Mondal, S., Mandal, S., & Mondal, S. (2019). Frequency ratio (FR) model and modified information value (MIV) model in landslide susceptibility assessment and prediction. Statistical Approaches for Landslide Susceptibility Assessment and Prediction, 77-105. https://doi.org/10.1007/978-3-319-93897-4_3
• Okoli, J., Nahazanan, H., Nahas, F., Kalantar, B., Shafri, H. Z. M., & Khuzaimah, Z. (2023). High-Resolution lidar-derived DEM for landslide susceptibility assessment using AHP and fuzzy logic in Serdang, Malaysia. Geosciences, 13(2), 34. https://doi.org/10.3390/geosciences13020034
• Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123. https://doi.org/10.29128/geomatik.1136951
• Pourghasemi, H. R., Mohammady, M., & Pradhan, B. (2012). Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97, 71-84. https://doi.org/10.1016/j.catena.2012.05.005
• Achour, Y., Boumezbeur, A., Hadji, R., Chouabbi, A., Cavaleiro, V., & Bendaoud, E. A. (2017). Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian Journal of Geosciences, 10, 194. https://doi.org/10.1007/s12517-017-2980-6
• Kohno, M., & Higuchi, Y. (2023). Landslide susceptibility assessment in the Japanese archipelago based on a landslide distribution map. ISPRS International Journal of Geo-Information, 12(2), 37. https://doi.org/10.3390/ijgi12020037
• Zhu, L., & Huang, J. F. (2006). GIS-based logistic regression method for landslide susceptibility mapping in regional scale. Journal of Zhejiang University-Science A, 7(12), 2007-2017. https://doi.org/10.1631/jzus.2006.A2007
• Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides, 9(1), 93-106. https://doi.org/10.1007/s10346-011-0283-7
• Rasyid, A. R., Bhandary, N. P., & Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3, 1-16. https://doi.org/10.1186/s40677-016-0053-x
• Polykretis, C., & Chalkias, C. (2018). Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Natural Hazards, 93, 249-274. https://doi.org/10.1007/s11069-018-3299-7
• Wubalem, A., & Meten, M. (2020). Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Applied Sciences, 2, 1-19. https://doi.org/10.1007/s42452-020-2563-0
• Sekarlangit, N., Fathani, T. F., & Wilopo, W. (2022). Landslide susceptibility mapping of Menoreh Mountain using logistic regression. Journal of Applied Geology, 7(1), 51-63. https://doi.org/10.22146/jag.72067
• Yadav, M., Pal, S. K., Singh, P. K., & Gupta, N. (2023). Landslide susceptibility zonation mapping using frequency ratio, information value model, and logistic regression model: a case study of Kohima district in Nagaland, India. In Landslides: Detection, Prediction and Monitoring: Technological Developments, 333-363. https://doi.org/10.1007/978-3-031-23859-8_17
• Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50, 847-855. https://doi.org/10.1007/s00254-006-0256-7
• Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35(6), 1125-1138. https://doi.org/10.1016/j.cageo.2008.08.007
• Akinci, H., Doğan, S., Kiliçoğlu, C., & Temiz, M. S. (2011). Production of landslide susceptibility map of Samsun (Turkey) City Center by using frequency ratio method. International Journal of the Physical Sciences, 6(5), 1015-1025. https://doi.org/10.5897/IJPS11.133
• Karaman, M. O., Çabuk, S. N., & Pekkan, E. (2022). Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey. Environmental Science and Pollution Research, 29(60), 91285-91305. https://doi.org/10.1007/s11356-022-21931-2
• Addis, A. (2023). GIS‐Based landslide susceptibility mapping using frequency ratio and Shannon Entropy Models in Dejen District, Northwestern Ethiopia. Journal of Engineering, 2023(1), 1062388. https://doi.org/10.1155/2023/1062388
• Ba, Q., Chen, Y., Deng, S., Wu, Q., Yang, J., & Zhang, J. (2017). An improved information value model based on gray clustering for landslide susceptibility mapping. ISPRS International Journal of Geo-Information, 6(1), 18. https://doi.org/10.3390/ijgi6010018
• Du, G. L., Zhang, Y. S., Iqbal, J., Yang, Z. H., & Yao, X. (2017). Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science, 14, 249-268. https://doi.org/10.1007/s11629-016-4126-9
• Mandal, B., & Mandal, S. (2017). Landslide susceptibility mapping using modified information value model in the Lish river basin of Darjiling Himalaya. Spatial Information Research, 25, 205-218. https://doi.org/10.1007/s41324-017-0096-4
• Khan, H., Shafique, M., Khan, M. A., Bacha, M. A., Shah, S. U., & Calligaris, C. (2019). Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 22(1), 11-24. https://doi.org/10.1016/j.ejrs.2018.03.004
• Jadda, M., Shafri, H. Z., Mansor, S. B., Sharifikia, M., & Pirasteh, S. (2009). Landslide susceptibility evaluation and factor effect analysis using probabilistic-frequency ratio model. European Journal of Scientific Research, 33(4), 654-668.
• 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. International Journal of Environmental Science and Technology, 11, 909-926. https://doi.org/10.1007/s13762-013-0464-0
• Liu, L. L., Zhang, Y. L., Xiao, T., & Yang, C. (2022). A frequency ratio–based sampling strategy for landslide susceptibility assessment. Bulletin of Engineering Geology and the Environment, 81(9), 360. https://doi.org/10.1007/s10064-022-02836-3
• Alam, A., Ahmed, B., Sammonds, P., & Kamal, A. M. (2023). Applying rainfall threshold estimates and frequency ratio model for landslide hazard assessment in the coastal mountain setting of South Asia. Natural Hazards Research, 3(3), 531-545. https://doi.org/10.1016/j.nhres.2023.08.002
• Singh, P., Sur, U., Rai, P. K., & Singh, S. K. (2023). Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India). Proceedings of the Indian National Science Academy, 89(3), 600-612. https://doi.org/10.1007/s43538-023-00171-z
• Thambidurai, P., Veerappan, R., Beigh, I. H., & Luitel, K. K. (2023). Landslide susceptibility assessment using frequency ratio model in Turung Mamring, south district of Sikkim, India. In Landslides: Detection, Prediction and Monitoring: Technological Developments, 285-305. https://doi.org/10.1007/978-3-031-23859-8_14
• Kadi, F. (2024). Statistical-based models for the production of landslide susceptibility maps and general risk analyses: a case study in Maçka, Turkey. Acta Geophysica, 1-26. https://doi.org/10.1007/s11600-024-01380-w
• Tampekis, S., Sakellariou, S., Samara, F., Sfougaris, A., Jaeger, D., & Christopoulou, O. (2015). Mapping the optimal forest road network based on the multicriteria evaluation technique: the case study of Mediterranean Island of Thassos in Greece. Environmental Monitoring and Assessment, 187, 1-17. https://doi.org/10.1007/s10661-015-4876-9
• Picchio, R., Pignatti, G., Marchi, E., Latterini, F., Benanchi, M., Foderi, C., ... & Verani, S. (2018). The application of two approaches using GIS technology implementation in forest road network planning in an Italian mountain setting. Forests, 9(5), 277. https://doi.org/10.3390/f9050277
• Çölkuşu, T., & Buğday, E. (2022). Planning optimal forest road network using unmanned aerial vehicle (Eldivan Sample). 1st International Karatekin Science and Technology Conference, 199-204.
• Taş, İ., Kaska, M. S., & Akay, A. E. (2023). Assessment of using UAV photogrammetry based DEM and ground-measurement based DEM in computer-assisted forest road design. European Journal of Forest Engineering, 9(1), 1-9. https://doi.org/10.33904/ejfe.1312514
• Boston, K. (2016). The potential effects of forest roads on the environment and mitigating their impacts. Current Forestry Reports, 2, 215-222. https://doi.org/10.1007/s40725-016-0044-x
• AFAD, (2019). Overview of Disaster Management and Natural Disaster Statistics. https://en.afad.gov.tr/kurumlar/en.afad/Afet_Istatistikleri_2020_eng_1.pdf
• Akgün, A. (2018). Bulanık uyarlanabilir rezonans teorisi (FuzzyART) yöntemi kullanılarak heyelan duyarlılık analizi: Tonya (Trabzon) Örneği. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 8(1), 135-146. https://doi.org/10.17714/gumusfenbil.346532
• http://www.tonya.gov.tr/ilcemizin-cografi-durumuu
• Abeysiriwardana, H. D., & Gomes, P. I. (2022). Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: using logistic regression. Journal of Mountain Science, 19(2), 477-492. https://doi.org/10.1007/s11629-021-6988-8
• Wang, X., Huang, F., Fan, X., Shahabi, H., Shirzadi, A., Bian, H., ... & Chen, W. (2022). Landslide susceptibility modeling based on remote sensing data and data mining techniques. Environmental Earth Sciences, 81(2), 50. https://doi.org/10.1007/s12665-022-10195-1
• Kanwal, S., Atif, S., & Shafiq, M. (2017). GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomatics, Natural Hazards and Risk, 8(2), 348-366. https://doi.org/10.1080/19475705.2016.1220023
• Ali, S. A., Parvin, F., Vojteková, J., Costache, R., Linh, N. T. T., Pham, Q. B., ... & Ghorbani, M. A. (2021). GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geoscience Frontiers, 12(2), 857-876. https://doi.org/10.1016/j.gsf.2020.09.004
• Melese, T., Belay, T., & Andemo, A. (2022). Application of analytical hierarchal process, frequency ratio, and Shannon entropy approaches for landslide susceptibility mapping using geospatial technology: The case of Dejen district, Ethiopia. Arabian Journal of Geosciences, 15(5), 424. https://doi.org/10.1007/s12517-022-09672-5
• Rabby, Y. W., Li, Y., & Hilafu, H. (2023). An objective absence data sampling method for landslide susceptibility mapping. Scientific Reports, 13(1), 1740. https://doi.org/10.1038/s41598-023-28991-5
• Yılmaz, O. S. (2023). Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatik, 8(1), 42-54. https://doi.org/10.29128/geomatik.1108735
• Yalcin, A., & Bulut, F. (2007). Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Natural Hazards, 41(1), 201-226. https://doi.org/10.1007/s11069-006-9030-0
• Yilmaz, O. S. (2022). Flood hazard susceptibility areas mapping using Analytical Hierarchical Process (AHP), Frequency Ratio (FR) and AHP-FR ensemble based on Geographic Information Systems (GIS): A case study for Kastamonu, Türkiye. Acta Geophysica, 70(6), 2747-2769. https://doi.org/10.1007/s11600-022-00882-9
• Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3-30. https://doi.org/10.1002/hyp.3360050103
• Wang, L. J., Guo, M., Sawada, K., Lin, J., & Zhang, J. (2016). A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosciences Journal, 20, 117-136. https://doi.org/10.1007/s12303-015-0026-1
• Sun, X., Chen, J., Han, X., Bao, Y., Zhou, X., & Peng, W. (2020). Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification. Bulletin of Engineering Geology and the Environment, 79, 4657-4670. https://doi.org/10.1007/s10064-020-01849-0
• Yılmaz, Ç. Ş. (2022). Improving the land cover mapping accuracy of the Sentinel-2 imagery on Google Earth Engine. Türk Uzaktan Algılama ve CBS Dergisi, 3(2), 150-159. https://doi.org/10.48123/rsgis.1119572
• Althuwaynee, O. F., Pradhan, B., Park, H. J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21-36. https://doi.org/10.1016/j.catena.2013.10.011
• Gudiyangada Nachappa, T., Kienberger, S., Meena, S. R., Hölbling, D., & Blaschke, T. (2020). Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 11(1), 572-600. https://doi.org/10.1080/19475705.2020.1736190
• Hussain, S., Mubeen, M., Akram, W., Ahmad, A., Habib-ur-Rahman, M., Ghaffar, A., ... & Nasim, W. (2020). Study of land cover/land use changes using RS and GIS: a case study of Multan district, Pakistan. Environmental Monitoring and Assessment, 192, 1-15. https://doi.org/10.1007/s10661-019-7959-1
• Chen, X., & Chen, W. (2021). GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena, 196, 104833. https://doi.org/10.1016/j.catena.2020.104833
• Hakim, W. L., Rezaie, F., Nur, A. S., Panahi, M., Khosravi, K., Lee, C. W., & Lee, S. (2022). Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea. Journal of Environmental Management, 305, 114367. https://doi.org/10.1016/j.jenvman.2021.114367
• Chen, W., & Yang, Z. (2023). Landslide susceptibility modeling using bivariate statistical-based logistic regression, naïve Bayes, and alternating decision tree models. Bulletin of Engineering Geology and the Environment, 82(5), 190. https://doi.org/10.1007/s10064-023-03216-1
• Yu, C., Lee, J. A. Y., & Munro-Stasiuk, M. J. (2003). Extensions to least-cost path algorithms for roadway planning. International Journal of Geographical Information Science, 17(4), 361-376. https://doi.org/10.1080/1365881031000072645
• Effat, H. A., & Hassan, O. A. (2013). Designing and evaluation of three alternatives highway routes using the Analytical Hierarchy Process and the least-cost path analysis, application in Sinai Peninsula, Egypt. The Egyptian Journal of Remote Sensing and Space Science, 16(2), 141-151. https://doi.org/10.1016/j.ejrs.2013.08.001
• McDonald, M. D., & Kessler, F. C. (2022). Least-Cost path and accessibility analysis of a high speed railway corridor: Victorville, CA to Las Vegas, NV. Journal of Geographic Information System, 14(1), 40-60. https://doi.org/10.4236/jgis.2022.141003
• Sawant, S., & Sawant, S. (2023). Finding optimal path for gas pipeline using GIS and RS. Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, 2, 321-333. https://doi.org/10.1007/978-981-19-2358-6_31