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Landslide Susceptibility Mapping: Comparison of Knowledge-driven AHP-FR and Data-driven SVM Methods in Zarab-Sanandaj Sub-basins, Kurdistan, Iran

Document Type : Original Research Paper

Authors

1 Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

2 College of Engineering, University of Tehran, Tehran, Iran

Abstract

Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.

Keywords

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References
  • Abdo, H. G. (2022). Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria. International Journal of Environmental Science and Technology, 19(4), 2599-2618.
  • Abella, E. A. C., & Van Westen, C. J. (2008). Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba. Geomorphology, 94(3-4), 453-466.
  • Achu, A. L., Aju, C. D., Pham, Q. B., Reghunath, R., & Anh, D. T. (2022). Landslide susceptibility modelling using hybrid bivariate statistical-based machine-learning method in a highland segment of Southern Western Ghats, India. Environmental Earth Sciences, 81(13), 360.
  • Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077-1095.
  • Akbari, S., & Ramazi, H. (2023). Application of AHP-SWOT and geophysical methods to develop a reasonable planning for Zagheh tourist destination considering environmental criteria. International Journal of Environmental Science, 8.
  • Akbari, S., Ramazi, H., & Ghezelbash, R. (2023). Using fractal and multifractal methods to reveal geophysical anomalies in Sardouyeh District, Kerman, Iran. Earth Science Informatics, 16(3), 2125-2142.
  • Akbari, S., Ramazi, H., & Ghezelbash, R. (2024). A data-driven VIKOR procedure for predictive modeling of porphyry copper prospectivity in SE Iran. Journal of Geochemical Exploration, 259, 107414.
  • Akbari, S., Ramazi, H., & Ghezelbash, R. (2025). A novel framework for optimizing the prediction of areas favorable to porphyry-cu mineralization: combination of ant colony and grid search optimization algorithms with support vector machines. Natural Resources Research, 1-27.
  • Akbari, S., Ramazi, H., Ghezelbash, R., & Maghsoudi, A. (2020). Geoelectrical integrated models for determining the geometry of karstic cavities in the Zarrinabad area, west of Iran: combination of fuzzy logic, CA fractal model and hybrid AHP-TOPSIS procedure. Carbonates and Evaporites, 35, 1-16.
  • Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering geology, 73(3-4), 247-265.
  • Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the environment, 58(1), 21-44.
  • Alimoradi, A., Angorani, S., Ebrahimzadeh, M., Shariat Panahi, M., (2011). Magnetic Inverse Modeling of a Dike Using the Artificial Neural Network Approach, The Journal of Near Surface Geophysics (EAGE), 9, 339-347.
  • Alimoradi, A., Moradzadeh, A., Bakhtiari, M. R., (2013). Application of Artificial Neural Networks and Support Vector Machines for Carbonate Pores Size Estimation from 3D Seismic Data, Journal of Mining and Environment, 4 (1), 1-14.
  • Alimoradi, A., Shahsavani, H., Kamkar Rohani, A., (2011). Prediction of Shear Wave Velocity in Underground Layers Using SASW and Artificial Neural Networks, The Journal of Engineering (Scientific Research Publishing), 3, 266-275.
  • Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1-2), 15-31.
  • Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P. M., & Derakhshani, R. (2021). Deep learning-based landslide susceptibility mapping. Scientific reports, 11(1), 24112.
  • 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, 115(1-2), 23-31.
  • Bednarik, M., Magulová, B., Matys, M., & Marschalko, M. (2010). Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study. Physics and Chemistry of the Earth, Parts A/B/C, 35(3-5), 162-171.
  • Bednarik, M., Yilmaz, I., & Marschalko, M. (2012). Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’landslide area in south-west Slovakia. Natural hazards, 64(1), 547-575.
  • Bhardwaj, V., & Singh, K. (2023). Landslide Susceptibility Assessment using Remote Sensing and GIS-a Review. Journal of Mining and Environment, 14(1), 133-154.
  • Broeckx, J., Vanmaercke, M., Duchateau, R., & Poesen, J. (2018). A data-based landslide susceptibility map of Africa. Earth-Science Reviews, 185, 102-121.
  • Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), 121-167.
  • Cao, Y., Wei, X., Fan, W., Nan, Y., Xiong, W., & Zhang, S. (2021). Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China. PLoS one, 16(1), e0245668.
  • Carranza, E. J. M., Van Ruitenbeek, F. J. A., Hecker, C., van der Meijde, M., & van der Meer, F. D. (2008). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation, 10(3), 374-387.
  • Castro-Miguel, R., Legorreta-Paulín, G., Bonifaz-Alfonzo, R., Aceves-Quesada, J. F., & Castillo-Santiago, M. Á. (2022). Modeling spatial landslide susceptibility in volcanic terrains through continuous neighborhood spatial analysis and multiple logistic regression in La Ciénega watershed, Nevado de Toluca, Mexico. Natural Hazards, 113(1), 767-788.
  • 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, 12(5), 340-350.
  • Chen, Wei, et al. "Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China." Science of the total environment626 (2018): 1121-1135.
  • Ciabatta, L., Camici, S., Brocca, L., Ponziani, F., Stelluti, M., Berni, N., & Moramarco, T. J. J. O. H. (2016). Assessing the impact of climate-change scenarios on landslide occurrence in Umbria Region, Italy. Journal of Hydrology, 541, 285-295.
  • Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2002). A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48(4), 349-364.
  • Conoscenti, C., Ciaccio, M., Caraballo-Arias, N. A., Gómez-Gutiérrez, Á., Rotigliano, E., & Agnesi, V. (2015). Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology, 242, 49-64.
  • Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
  • Dai, F. C., Lee, C. F., & Ngai, Y. Y. (2002). Landslide risk assessment and management: an overview. Engineering geology, 64(1), 65-87.
  • Dai, F. C., Lee, C. F., Li, J. X. Z. W., & Xu, Z. W. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3), 381-391.
  • Das, S., Sarkar, S., & Kanungo, D. P. (2022). GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya. Environmental Monitoring and Assessment, 194(4), 234.
  • Daviran, M., Shamekhi, M., Ghezelbash, R., & Maghsoudi, A. (2023). Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm. International Journal of Environmental Science and Technology, 20(1), 259-276.
  • Demir, G., Aytekin, M., Akgün, A., Ikizler, S. B., & Tatar, O. (2013). A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Natural hazards, 65(3), 1481-1506.
  • Dhakal, D., & Singh, K. (2025). A Geospatial Approach to Landslide Susceptibility Mapping of Spiti, India. Journal of Mining and Environment, Vol. 16, No. 1, 2025.
  • Dong, J. J., Lee, W. R., Lin, M. L., Huang, A. B., & Lee, Y. L. (2009). Effects of seismic anisotropy and geological characteristics on the kinematics of the neighboring Jiufengershan and Hungtsaiping landslides during Chi-Chi earthquake. Tectonophysics, 466(3-4), 438-457.
  • Dou, J., Tien Bui, D., P. Yunus, A., Jia, K., Song, X., Revhaug, I., ... & Zhu, Z. (2015). Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PloS one, 10(7), e0133262.
  • Duman, T. Y., Can, T., Gokceoglu, C., Nefeslioglu, H. A., & Sonmez, H. (2006). Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology, 51(2), 241-256.
  • Ercanoglu, M. U. R. A. T., Gokceoglu, C. A. N. D. A. N., & Van Asch, T. W. (2004). Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards, 32(1), 1-23.
  • 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.
  • Felicísimo, Á. M., Cuartero, A., Remondo, J., & Quirós, E. (2013). Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides, 10(2), 175-189.
  • Fernández, C. I., Del Castillo, T. F., Hamdouni, R. E., & Montero, J. C. (1999). Verification of landslide susceptibility mapping: a case study. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 24(6), 537-544.
  • Fressard, M., Thiery, Y., & Maquaire, O. (2014). Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France). Natural Hazards and Earth System Sciences, 14(3), 569-588.
  • Ghasemi Tabar, H. R., Alimoradi, A., Hemmati Ahooi, H. R., Fathi, M. (2023). Intelligent borehole simulation with python programming, Journal of Mining and Environment, doi: 10.22044/jme.2023.13610.2527.
  • Ghezelbash, R., & Maghsoudi, A. (2018). A hybrid AHP-VIKOR approach for prospectivity modeling of porphyry Cu deposits in the Varzaghan District, NW Iran. Arabian Journal of Geosciences, 11, 1-15.
  • Ghezelbash, R., Daviran, M., Maghsoudi, A., Ghaeminejad, H., & Niknezhad, M. (2023). Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran. Applied Geochemistry, 148, 105538.
  • Ghezelbash, R., Maghsoudi, A., & Daviran, M. (2019). Implementation of Fuzzy-AHP and Fuzzy-GAMMA approaches for discovering the prospectivity areas of Au mineralization in Takhte-Soleyman district. Researches in Earth Sciences, 10(1), 143-162.
  • Ghezelbash, R., Maghsoudi, A., Shamekhi, M., Pradhan, B., & Daviran, M. (2023). Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity. Neural Computing and Applications, 35(1), 719-733.
  • Gökceoglu, C., & Aksoy, H. Ü. S. E. Y. İ. N. (1996). Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology, 44(1-4), 147-161.
  • Gupta, R. P., Saha, A. K., Arora, M. K., & Kumar, A. (1999). Landslide Hazard Zonation in a part of the Bhagirathi Valley. Garhwal Mimalyas, using integrated remote sensing–GIS, Himalayan Geology, 20, 71-85.
  • Hemasinghe, H., Rangali, R. S. S., Deshapriya, N. L., & Samarakoon, L. (2018). Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka). Procedia engineering, 212, 1046-1053.
  • Hillman, A. L., Yu, J., Abbott, M. B., Cooke, C. A., Bain, D. J., & Steinman, B. A. (2014). Rapid environmental change during dynastic transitions in Yunnan Province, China. Quaternary Science Reviews, 98, 24-32.
  • Hong, H., Chen, W., Xu, C., Youssef, A. M., Pradhan, B., & Tien Bui, D. (2017). Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto international, 32(2), 139-154.
  • Hong, H., Pradhan, B., Xu, C., & Bui, D. T. (2015). Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena, 133, 266-281.
  • Huang, F., Chen, J., Liu, W., Huang, J., Hong, H., & Chen, W. (2022). Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold. Geomorphology, 408, 108236.
  • 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(4), 909-926.
  • Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2015). Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest: Cameron Highlands, Malaysia. Geomatics, Natural Hazards and Risk, 6(8), 741-759.
  • 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.
  • Kavzoglu, T., & Colkesen, I. (2014). A comparison of SVM and other classifiers for landslide susceptibility mapping. Environmental Earth Sciences, 72(6), 2011-2024. https://doi.org/10.1007/s12665-014-3636-0
  • Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11, 425-439.
  • Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2015). Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Engineering Geology, 192, 101-112.
  • Kayastha, P., Dhital, M. R., & De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Computers & Geosciences, 52, 398-408.
  • Kecman, V. (2005). Support vector machines–an introduction. In Support vector machines: theory and applications(pp. 1-47). Springer, Berlin, Heidelberg.
  • Keefer, D. K., Wilson, R. C., Mark, R. K., Brabb, E. E., Brown, W. M., Ellen, S. D., ... & Zatkin, R. S. (1987). Real-time landslide warning during heavy rainfall. Science, 238(4829), 921-925.
  • 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.
  • Kornejady, A., Heidari, K., & Nakhavali, M. (2015). Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran. Environmental Resources Research, 3(1), 85-109.
  • Kornejady, A., Ownegh, M., & Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152, 144-162.
  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491.
  • Lee S, Choi J, Min K (2004a) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. Int J Remote Sens 25:2037–2052.
  • Lee, C. H., & Hsu, S. H. (2017). Landslide susceptibility mapping using support vector machine: A case study in the Lushan, Taiwan area. Geomatics, Natural Hazards and Risk, 8(4), 346-361. https://doi.org/10.1080/19475705.2016.1217227
  • 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.
  • Lee, S., Hong, S. M., & Jung, H. S. (2017). A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability, 9(1), 48.
  • Leonardi, G., Palamara, R., & Cirianni, F. (2016). Landslide susceptibility mapping using a fuzzy approach. Procedia engineering, 161, 380-387.
  • Li, L., Lan, H., Guo, C., Zhang, Y., Li, Q., & Wu, Y. (2017). A modified frequency ratio method for landslide susceptibility assessment. Landslides, 14, 727-741.
  • Li, Z., Nadim, F., Huang, H., Uzielli, M., & Lacasse, S. (2010). Quantitative vulnerability estimation for scenario-based landslide hazards. Landslides, 7(2), 125-134.
  • Liu, Q., & Tang, A. (2022). Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology. Geocarto International, 37(26), 14547-14569.
  • Mahvash Mohammadi, N., & Hezarkhani, A. (2020). A comparative study of SVM and RF methods for classification of alteration zones using remotely sensed data. Journal of Mining and Environment, 11(1), 49-61.
  • Mandal, B., & Mandal, S. (2018). Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Advances in Space Research, 62(11), 3114-3132.
  • Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234.
  • Moosavi, M., & Pakdaman, A. M. (2024). New 2D joint roughness profiles based on pattern recognition technique. Bulletin of Engineering Geology and the Environment, 83(1), 4.
  • Ocakoglu F, Gokceoglu C, Ercanoglu M (2002) Dynamics of a complex mass movement triggered by heavy rainfall: a case study from NW Turkey. Geomorphology 42(3):329–341.
  • Oliveira, S. C., Zêzere, J. L., Catalão, J., & Nico, G. (2015). The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas. Landslides, 12(4), 703-719.
  • Pachauri, A. K., Gupta, P. V., & Chander, R. (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology, 36(3), 325-334.
  • Pakdaman, A. M., & Moosavi, M. (2023). Surface roughness assessment of natural rock joints based on an unsupervised pattern recognition technique using 2D profiles. Rudarsko-geološko-naftni zbornik, 38(2), 185-198.
  • Pakdaman, A. M., & Moosavi, M. (2024). Determination of surface roughness of rocks based on 2D profiles using machine learning methods. Archive of Applied Mechanics, 94(1), 157-185.
  • Park, S., Choi, C., Kim, B., & Kim, J. (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental earth sciences, 68, 1443-1464.
  • Pourghasemi, H. R., & Kerle, N. (2016). Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental earth sciences, 75(3), 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. Theoretical and Applied Climatology, 130(1), 609-633.
  • Pourghasemi, H. R., Gayen, A., Park, S., Lee, C. W., & Lee, S. (2018). Assessment of landslide-prone areas and their zonation using logistic regression, logitboost, and naïvebayes machine-learning algorithms. Sustainability, 10(10), 3697.
  • Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards, 63(2), 965-996.
  • Pradhan B (2010a) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45:1244–1256.
  • Pradhan B (2010b) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci. doi:10.1007/s12665-010-0705-1.
  • Pradhan, B. (2010). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38(2), 301-320.
  • Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350-365.
  • Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747-759.
  • Rai, D. K., Xiong, D., Zhao, W., Zhao, D., Zhang, B., Dahal, N. M., ... & Baig, M. A. (2022). An investigation of landslide susceptibility using logistic regression and statistical index methods in Dailekh District, Nepal. Chinese Geographical Science, 32(5), 834-851.
  • Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-science reviews, 180, 60-91.
  • Reichenbach, P., Busca, C., Mondini, A. C., & Rossi, M. (2014). The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environmental management, 54, 1372-1384.
  • Remondo, J., Bonachea, J., & Cendrero, A. (2005). A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment. Landslides, 2, 321-328.
  • Ruff, M., & Czurda, K. (2008). Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology, 94(3-4), 314-324.
  • Saaty, T. L. (1994). How to make a decision: the analytic hierarchy process. Interfaces, 24(6), 19-43.
  • Saha, A. K., Gupta, R. P., & Arora, M. K. (2002). GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. International journal of remote sensing, 23(2), 357-369.
  • Saha, A. K., Gupta, R. P., Sarkar, I., Arora, M. K., & Csaplovics, E. (2005). An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides, 2(1), 61-69.
  • Sarina Akbari, Hamidreza Ramazi. (2023) Application of AHP -SWOT and Geophysical Methods to Develop a Reasonable Planning for Zagheh Tourist Destination Considering Environmental Criteria. International Journal of Environmental Science, 8, 11-56.
  • Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific reports, 5(1), 1-15.
  • Shahabi, H., Hashim, M., & Ahmad, B. B. (2015). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environmental Earth Sciences, 73(12), 8647-8668.
  • Shahri, A. A., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183, 104225.
  • Singh, A., Pal, S., & Kanungo, D. P. (2021). An integrated approach for landslide susceptibility–vulnerability–risk assessment of building infrastructures in hilly regions of India. Environment, Development and Sustainability, 23(4), 5058-5095.
  • Taner San, B. (2014). An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey). International journal of applied earth observation and geoinformation, 26, 399-412.
  • 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, 145, 164-179.
  • Tsangaratos, P., Ilia, I., Hong, H., Chen, W., & Xu, C. (2017). Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides, 14(3), 1091-1111.
  • Van Den Eeckhaut, M., & Hervás, J. (2012). State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk. Geomorphology, 139, 545-558.
  • Van Westen, C. J., & Alzate Bonilla, J. B. (1990). Mountain hazard analysis using a PC-based GIS. In International congress international association of engineering geology. 6(pp. 265-271).
  • Van Westen, C. J., Rengers, N., & Soeters, R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural hazards, 30, 399-419.
  • Vapnik V N 1995 The Nature of Statistical Learning Theory (New York: Springer Verlag).
  • Wang, L. J., 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. Catena, 135, 271-282.
  • Wang, Z., Wang, D., Guo, Q., & Wang, D. (2020). Regional landslide hazard assessment through integrating susceptibility index and rainfall process. Natural Hazards, 104(3), 2153-2173.
  • Wind, Y., & Saaty, T. L. (1980). Marketing applications of the analytic hierarchy process. Management science, 26(7), 641-658.
  • Xu, C., Dai, F., Xu, X., & Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70-80.
  • Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 72(1), 1-12.
  • 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.
  • 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, 101(4), 572-582.
  • 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.
  • Yilmaz, I. (2010). Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4), 821-836.
  • 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. Geosciences Journal, 19(1), 113-134.
  • Yu, X., Zhang, K., Song, Y., Jiang, W., & Zhou, J. (2021). Study on landslide susceptibility mapping based on rock–soil characteristic factors. Scientific reports, 11(1), 15476.
  • Zhang, C., Li, Z., Yu, C., Chen, B., Ding, M., Zhu, W., ... & Peng, J. (2022). An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets. Landslides, 19(12), 2905-2923.
  • Zuo, R., & Carranza, E. J. M. (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 37(12), 1967-1975.
Journal of Mining and Environment
Volume 17, Issue 1
January and February 2026
Pages 261-282
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