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Asgari S, shadfar S. Landslide risk zoning using artificial neural network (ANN) in Mishkhas watershed of Ilam. Journal of Spatial Analysis Environmental Hazards 2025; 11 (4)
URL: http://jsaeh.khu.ac.ir/article-1-3453-fa.html
URL: http://jsaeh.khu.ac.ir/article-1-3453-fa.html
عسگری شمس اله، شادفر صمد. پهنه بندی خطر زمین لغزش با استفاده از شبکه عصبی مصنوعی در حوضه آبخیز میشخاص ایلام. تحلیل فضایی مخاطرات محیطی. ۱۴۰۳; ۱۱ (۴)
۱- استادیار بخش تحقیقات منابع طبیعی و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان ایلام، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران. ، Shamsasgari@yahoo.com
۲- دانشیار پژوهشکده حفاظت خاک و آبخیزداری ، سازمان تحقیقات، آموزش و ترویج کشاورزی تهران، ایران.
۲- دانشیار پژوهشکده حفاظت خاک و آبخیزداری ، سازمان تحقیقات، آموزش و ترویج کشاورزی تهران، ایران.
چکیده: (۴۲۹ مشاهده)
زﻣﻴﻦﻟﻐﺰشﻫﺎ، یکی از مخاطرات طبیعی هستند که جان و مال انسانها را تهدید می کنند. زمین لغزش ممکن است در یک منطقه ده‌ها، صدها و شاید هزارن هکتار از اراضی را در زمانی کوتاه تخریب نماید. این مخاطره سالهاست که در منطقه کوهستانی میشخاص استان ایلام، اراضی باغ میوه، مزارع، عرصه‌های جنگلی و مراتع، راه‌های ارتباطی، سکونتگاه‌های روستایی را تخریب نموده است. پهنه‌بندی خطر زمین‌لغزش جهت کنترل این مخاطره در این حوضه ضرورت دارد. هدف اصلی این تحقیق پهنه‌بندی مناطق خطر زمین‌لغزش در این حوضه آبخیز می باشد. یکی از روش‌های نوین جهت بررسی خطر زمین‌لغزش، روش شبکه عصبی مصنوعی می‌باشد .این روش نسبت به روش‌های دیگر دارای مزیت‌هایی است، توزیع آماری داده‌ها مستقل است و به متغیرهای آماری مخصوصی نیاز ندارد. در این تحقیق، ابتدا اقدام به تهیه نقشه پراکنش زمین‌لغزش در حوضه انتخابی گردید. سپس ارتباط بین متغیرهای مستقل مانند شیب، سنگ شناسی، فاصله از گسل، کاربری اراضی، فاصله از شبکه راه‌ها، فاصله از آبراهه‌ها، جهت شیب با مناطق تحت‌تاثیر زمین لغزش مورد بررسی قرار گرفت. پس از تهیه نقشه‌های وزنی، این لایه‌ها در محیط نرم‌افزار ArcGIS به اطلاعات عددی تبدیل و پس از استاندارد کردن به نرم افزار MATLAB وارد شده و برنامه‌ای با ساختار پرسپترون با الگوریتم یادگیری پس انتشار خطا، نوشته شد. بعد از مشخص شدن ساختار شبکه عصبی مصنوعی و آموزش و آزمایش آن، نتایج مورد ارزیابی و خروجی شبکه در محیط سیستم‌های اطلاعات جغرافیایی، تبدیل به نقشه خطر زمین لغزش شد. نقشه خطر حاصله به پهنه‌های مختلف خطر، طبقه‌بندی و مقدار زمین‌لغزش در هر پهنه آن محاسبه گردید. نتایج حاصل از بررسی عوامل نشان داد که در حوضه میشخاص ایلام سازند آسماری، طبقه شیب 10تا20 درصد، طبقه فاصله از گسل بیشتر از 500 متر، جهت شمال شرق، فاصله از آبراهه‌های بیشتر از 100 متر، باغات میوه حساس‌ترین کاربری‌ها و فاصله ازجاده بیشتر از 200 متر، حساس‌ترین طبقات نسبت به وقوع زمین‌لغزش و دارای بیشترین نسبت فراوانی وقوع زمین‌لغزش در حوضه می باشند. از سوی دیگر، نتایج حاصل از پهنه‌بندی خطر زمین‌لغزش با استفاده از روش شبکه عصبی مصنوعی نشان داد در حوضه میشخاص ایلام حدود 80 درصد زمین لغزش‌ها در پهنه‌های خطر زیاد و خیلی زیاد قرار گرفته‌اند.
فهرست منابع
1. Bakhtiyari, M., komeh, Z., Memarian, H. (2018). 'A Comparison of Fuzzy Analytic Hierarchy Process, Artificial Neural Network and Area Density in Quantitative Evaluation and Landslide Susceptibility Mapping within GIS Framework (Case Study: Simereh Homiyan Watershed), Journal of Geography and Environmental Hazards, 7(3), 19-40.(InPersian).https://doi.org/ 10.22067/geo.v0i0.67234
2. 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,29 (45):55–72. https://doi.org/10.1007/s11069-007-9169-3
3. Chen H., G.W. Lin, M.H. Lu, T.Y. Shih, M.J. Horng, S.J. Wu, B. Chuang. 2011. Effects of topography, lithology, rainfall and earthquake on landslide and sediment discharge in mountain catchments of southeastern Taiwan. Geomorphology 133, 132–142. https://doi.org/10.1016/j. geomorphic. 2010. 12. 031
4. 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, 113, 236-250. https://doi.org/10.1016/j.catena.2013.08.006
5. Crosta, G., & Clague, J.J. (2009). Dating, triggering modeling, and hazard assessment of large, landslides, Geomorphology, 103(1): 1-4. https://doi.org/10.1016/j.geomorph.2008.04.007
6. Dai, K. R., Z. H. Li, Q. Xu, R. Burgmann, D. G. Milledge, R. Tomas, X. M. Fan, et al. 2020. “Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework.” IEEE Geoscience and Remote Sensing Magazine 8 (1),136–153. https://doi.org/10.1109/MGRS.2019.2954395
7. Emaduddin, S., Moradi, A., (2017). "Evaluation of landslide risk using hierarchical process (AHP), artificial neural network (ANN) analysis and field studies with risk reduction approach (case study: Haraz road axis)", Quantitative Geomorphology Research, 6(4), 172-190. (In Persian) https://doi.org/20.1001.1.22519424.1397.6.4.12.9
8. Erener, A., Sarp, G., & Duzgun, S. (2019). Use of GIS and remote sensing for landslide susceptibility mapping, Advanced Methodologies and Technologies in Engineering and Environmental Science,26(8), 384-398. https://doi.org/10.4018/978-1-5225-7359-3.ch026
9. Gomez H., Kavzoglu T., 2005: Assessment of shallow landslide susceptibility using artificial lneural networks in Jabonosa River Basin, Venezuela, Engineering Geology,78(1–2):11–27. https://doi.org/10.1016/j.enggeo.2004.10.004
10. He, Y., Zhao, Z., Zhu, Q., Liu, T., Zhang, Q., Yang, W., Wang, Q. (2023). An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features,InternationalJournalofDigitalEarth,17(1),136.152.https://doi.org/10.1080/17538947.2023.2295408
11. Huang, F.M., Cao, Z.S., Guo, J.F., Jiang, S.H., Li, S., Guo, Z.Z., 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena 191, 104580. https://doi.org/10.1016/j.catena.2020.104580
12. Khan, A., Gupta, S., & Gupta, S. K. 2020. Multihazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International Journal of Disaster Risk Reduction, 47(4): 31–53. https://doi.org/10.1016/j.ijdrr.2020.101642
13. Klarstaghi Atalae, Habib Nejadroshan, Mahmoud and Ahmadi Hassan, 2007, study of the occurrence of landslides in connection with the change of land use and road construction, a case study of the Tajen watershed, Sari, Geographical Researches, 39: (62), 81-91. (In Persian). https://doi.org/10.1080/17538947.2007.2295408
14. Lee S., Ryu J. H., Lee M. J., Won J. S., 2003: Use of an Artificial Neural Network for analysis of the susceptibility to landslides at Boun, Korea, Environmental Geology, 44(7), 820–833. https://doi.org/10.1007/s00254-003-0825-y
15. Lee S., Ryu J. H., Lee M. J., Won J. S., 2006: The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, 38(2),199-220. https://doi.org/10.1007/s11004-005-9012-x
16. Lee S., Ryu J. H., Won J. S., Park H. J., 2004: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Engineering Geology, 71(8), 289–302. https://doi.org/10.1016/S0013-7952(03)00142-X
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 (6), 847–855. https://doi.org/10.1007/s00254-006-0256-7
18. Lee. S., Chwae, U., Min, K. 2002. Landslide susceptibility mapping by correlation between topograghy and geological structure:the Janghung area,Korea. Geomorphology, 46: 149-162. https://doi.org/ 10.1016/S0169-555X(02)00057-0
19. Menhaj Mohammad Baqer, (2021) Basics of Neural Networks, Publications of Amir Kabir University of Technology (Tehran Polytechnic), 1(11), 715 pages.
20. Mantovani, J. R., G. T. Bueno, E. Alcântara, E. Park, A. P. Cunha, L. Londe, K. Massi, and J. A. Marengo. 2023. “Novel Landslide Susceptibility Mapping Based on Multi-Criteria Decision-Making in Ouro Preto, Brazil.” Journal of Geovisualization and Spatial Analysis 7 (1),71-92. https://doi.org/10.1007/s41651-023-00138-0
21. Moghimi, Ibrahim. Ulumbanah, Seyyed Kazem and Jafari, Timur. (2009). Evaluation and zoning of factors affecting the occurrence of landslides in the northern slopes of Aladagh. Case study: Chenaran drainage basin in North Khorasan province, Institute of Geography, University of Tehran, Journal of Geographical Research, 64(9), 53 - 77. [In Persian]. https://doi.org/10.22059/JPHGR.2009.355408.1007750
22. Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzymodel to landslid-susceptibility mapping for shallow landslides in a tropical hilly area, Computers & Geosciences, 37(9), 1264-1276.
23. Rajabi, A M., Khosravi, H., 2019 The Zoning of Earthquake-Induced Earthquake Hazards using the AHP Model. Journal of Engineering Geology; 12 (4):635-658. https://doi.org/10.18869/acadpub.jeg.12.4.635
24. Shadfar, Samad, 2016, investigation of factors affecting landslide and its zoning using GIS in Peltan watershed, 3rd Conference of Spatial Information Systems, Qeshm, (In Persian).
25. https://civilica.com/doc/10889
26. Shirani, K., Naderi Samani, R. (2022). 'Determination of Effective factors and Assessment of Landslide Susceptibility Using Random Forest and Artificial Neural Network in Doab Samsami Region, Chaharmahal va Bakhtiari Province', Watershed Management Research Journal, 35(1), 40-60,(In Persian). https://doi.org/ 10.22092/wmrj.2021.354962.1421
27. Xu, C., Xu, X., Dai, F., & Saraf, A.K. (2012). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 wenchuan earthquake in china. Computers & Geosciences, 46, 317-329. https://doi.org/10.1016/j.cageo.2012.01.002
28. Yalcin, A., Reis, S., Aydinoglu, A., & Yomralioglu, 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, 85(3), 274-287. https://doi.org/10.1016/j.catena.2011.01.014
29. 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 and Geosciences, 35: 1125 – 1138. https://doi.org/10.1016/j.cageo.2008.08.007
59. Bakhtiyari, M., komeh, Z., Memarian, H. (2018). 'A Comparison of Fuzzy Analytic Hierarchy Process, Artificial Neural Network and Area Density in Quantitative Evaluation and Landslide Susceptibility Mapping within GIS Framework (Case Study: Simereh Homiyan Watershed), Journal of Geography and Environmental Hazards, 7(3), 19-40.(InPersian).https://doi.org/ 10.22067/geo.v0i0.67234 [DOI:10.22067/geo.v0i0.67234]
60. 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,29 (45):55–72. [DOI:10.1007/s11069-007-9169-3]
61. Chen H., G.W. Lin, M.H. Lu, T.Y. Shih, M.J. Horng, S.J. Wu, B. Chuang. 2011. Effects of topography, lithology, rainfall and earthquake on landslide and sediment discharge in mountain catchments of southeastern Taiwan. Geomorphology 133, 132–142. [DOI:10.1016/j. geomorphic. 2010. 12. 031]
62. 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, 113, 236-250. [DOI:10.1016/j.catena.2013.08.006]
63. Crosta, G., & Clague, J.J. (2009). Dating, triggering modeling, and hazard assessment of large, landslides, Geomorphology, 103(1): 1-4. [DOI:10.1016/j.geomorph.2008.04.007]
64. Dai, K. R., Z. H. Li, Q. Xu, R. Burgmann, D. G. Milledge, R. Tomas, X. M. Fan, et al. 2020. “Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework.” IEEE Geoscience and Remote Sensing Magazine 8 (1),136–153. [DOI:10.1109/MGRS.2019.2954395]
65. Emaduddin, S., Moradi, A., (2017). "Evaluation of landslide risk using hierarchical process (AHP), artificial neural network (ANN) analysis and field studies with risk reduction approach (case study: Haraz road axis)", Quantitative Geomorphology Research, 6(4), 172-190. (In Persian) [DOI:20.1001.1.22519424.1397.6.4.12.9]
66. Erener, A., Sarp, G., & Duzgun, S. (2019). Use of GIS and remote sensing for landslide susceptibility mapping, Advanced Methodologies and Technologies in Engineering and Environmental Science,26(8), 384-398. [DOI:10.4018/978-1-5225-7359-3.ch026]
67. Gomez H., Kavzoglu T., 2005: Assessment of shallow landslide susceptibility using artificial lneural networks in Jabonosa River Basin, Venezuela, Engineering Geology,78(1–2):11–27. [DOI:10.1016/j.enggeo.2004.10.004]
68. He, Y., Zhao, Z., Zhu, Q., Liu, T., Zhang, Q., Yang, W., Wang, Q. (2023). An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features,InternationalJournalofDigitalEarth,17(1),136.152. [DOI:10.1080/17538947.2023.2295408]
69. Huang, F.M., Cao, Z.S., Guo, J.F., Jiang, S.H., Li, S., Guo, Z.Z., 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena 191, 104580. [DOI:10.1016/j.catena.2020.104580]
70. Khan, A., Gupta, S., & Gupta, S. K. 2020. Multihazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International Journal of Disaster Risk Reduction, 47(4): 31–53. [DOI:10.1016/j.ijdrr.2020.101642]
71. Klarstaghi Atalae, Habib Nejadroshan, Mahmoud and Ahmadi Hassan, 2007, study of the occurrence of landslides in connection with the change of land use and road construction, a case study of the Tajen watershed, Sari, Geographical Researches, 39: (62), 81-91. (In Persian). [DOI:10.1080/17538947.2007.2295408]
72. Lee S., Ryu J. H., Lee M. J., Won J. S., 2003: Use of an Artificial Neural Network for analysis of the susceptibility to landslides at Boun, Korea, Environmental Geology, 44(7), 820–833. [DOI:10.1007/s00254-003-0825-y]
73. Lee S., Ryu J. H., Lee M. J., Won J. S., 2006: The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, 38(2),199-220. [DOI:10.1007/s11004-005-9012-x]
74. Lee S., Ryu J. H., Won J. S., Park H. J., 2004: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Engineering Geology, 71(8), 289–302. [DOI:10.1016/S0013-7952(03)00142-X]
75. Lee S., Sambath T., 2006: Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology 50 (6), 847–855. [DOI:10.1007/s00254-006-0256-7]
76. Lee. S., Chwae, U., Min, K. 2002. Landslide susceptibility mapping by correlation between topograghy and geological structure:the Janghung area,Korea. Geomorphology, 46: 149-162. https://doi.org/ 10.1016/S0169-555X(02)00057-0 [DOI:10.1016/S0169-555X(02)00057-0]
77. Menhaj Mohammad Baqer, (2021) Basics of Neural Networks, Publications of Amir Kabir University of Technology (Tehran Polytechnic), 1(11), 715 pages.
78. Mantovani, J. R., G. T. Bueno, E. Alcântara, E. Park, A. P. Cunha, L. Londe, K. Massi, and J. A. Marengo. 2023. “Novel Landslide Susceptibility Mapping Based on Multi-Criteria Decision-Making in Ouro Preto, Brazil.” Journal of Geovisualization and Spatial Analysis 7 (1),71-92. [DOI:10.1007/s41651-023-00138-0]
79. Moghimi, Ibrahim. Ulumbanah, Seyyed Kazem and Jafari, Timur. (2009). Evaluation and zoning of factors affecting the occurrence of landslides in the northern slopes of Aladagh. Case study: Chenaran drainage basin in North Khorasan province, Institute of Geography, University of Tehran, Journal of Geographical Research, 64(9), 53 - 77. [In Persian]. [DOI:10.22059/JPHGR.2009.355408.1007750]
80. Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzymodel to landslid-susceptibility mapping for shallow landslides in a tropical hilly area, Computers & Geosciences, 37(9), 1264-1276.
81. Rajabi, A M., Khosravi, H., 2019 The Zoning of Earthquake-Induced Earthquake Hazards using the AHP Model. Journal of Engineering Geology; 12 (4):635-658. [DOI:10.18869/acadpub.jeg.12.4.635]
82. Shadfar, Samad, 2016, investigation of factors affecting landslide and its zoning using GIS in Peltan watershed, 3rd Conference of Spatial Information Systems, Qeshm, (In Persian).
83. https://civilica.com/doc/10889
84. Shirani, K., Naderi Samani, R. (2022). 'Determination of Effective factors and Assessment of Landslide Susceptibility Using Random Forest and Artificial Neural Network in Doab Samsami Region, Chaharmahal va Bakhtiari Province', Watershed Management Research Journal, 35(1), 40-60,(In Persian). https://doi.org/ 10.22092/wmrj.2021.354962.1421 [DOI:10.22092/wmrj.2021.354962.1421]
85. Xu, C., Xu, X., Dai, F., & Saraf, A.K. (2012). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 wenchuan earthquake in china. Computers & Geosciences, 46, 317-329. [DOI:10.1016/j.cageo.2012.01.002]
86. Yalcin, A., Reis, S., Aydinoglu, A., & Yomralioglu, 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, 85(3), 274-287. [DOI:10.1016/j.catena.2011.01.014]
87. 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 and Geosciences, 35: 1125 – 1138. [DOI:10.1016/j.cageo.2008.08.007]
88. Bakhtiyari, M., komeh, Z., Memarian, H. (2018). 'A Comparison of Fuzzy Analytic Hierarchy Process, Artificial Neural Network and Area Density in Quantitative Evaluation and Landslide Susceptibility Mapping within GIS Framework (Case Study: Simereh Homiyan Watershed), Journal of Geography and Environmental Hazards, 7(3), 19-40.(InPersian).https://doi.org/ 10.22067/geo.v0i0.67234 [DOI:10.22067/geo.v0i0.67234]
89. 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,29 (45):55–72.
90. Chen H., G.W. Lin, M.H. Lu, T.Y. Shih, M.J. Horng, S.J. Wu, B. Chuang. 2011. Effects of topography, lithology, rainfall and earthquake on landslide and sediment discharge in mountain catchments of southeastern Taiwan. Geomorphology 133, 132–142.
91. 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, 113, 236-250.
92. Crosta, G., & Clague, J.J. (2009). Dating, triggering modeling, and hazard assessment of large, landslides, Geomorphology, 103(1): 1-4.
93. Dai, K. R., Z. H. Li, Q. Xu, R. Burgmann, D. G. Milledge, R. Tomas, X. M. Fan, et al. 2020. “Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework.” IEEE Geoscience and Remote Sensing Magazine 8 (1),136–153.
94. Emaduddin, S., Moradi, A., (2017). "Evaluation of landslide risk using hierarchical process (AHP), artificial neural network (ANN) analysis and field studies with risk reduction approach (case study: Haraz road axis)", Quantitative Geomorphology Research, 6(4), 172-190. (In Persian)
95. Erener, A., Sarp, G., & Duzgun, S. (2019). Use of GIS and remote sensing for landslide susceptibility mapping, Advanced Methodologies and Technologies in Engineering and Environmental Science,26(8), 384-398.
96. Gomez H., Kavzoglu T., 2005: Assessment of shallow landslide susceptibility using artificial lneural networks in Jabonosa River Basin, Venezuela, Engineering Geology,78(1–2):11–27.
97. He, Y., Zhao, Z., Zhu, Q., Liu, T., Zhang, Q., Yang, W., Wang, Q. (2023). An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features,InternationalJournalofDigitalEarth,17(1),136.152.
98. Huang, F.M., Cao, Z.S., Guo, J.F., Jiang, S.H., Li, S., Guo, Z.Z., 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena 191, 104580.
99. Khan, A., Gupta, S., & Gupta, S. K. 2020. Multihazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International Journal of Disaster Risk Reduction, 47(4): 31–53.
100. Klarstaghi Atalae, Habib Nejadroshan, Mahmoud and Ahmadi Hassan, 2007, study of the occurrence of landslides in connection with the change of land use and road construction, a case study of the Tajen watershed, Sari, Geographical Researches, 39: (62), 81-91. (In Persian).
101. Lee S., Ryu J. H., Lee M. J., Won J. S., 2003: Use of an Artificial Neural Network for analysis of the susceptibility to landslides at Boun, Korea, Environmental Geology, 44(7), 820–833.
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131. Lee S., Ryu J. H., Lee M. J., Won J. S., 2006: The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, 38(2),199-220.
132. Lee S., Ryu J. H., Won J. S., Park H. J., 2004: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Engineering Geology, 71(8), 289–302.
133. Lee S., Sambath T., 2006: Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology 50 (6), 847–855.
134. Lee. S., Chwae, U., Min, K. 2002. Landslide susceptibility mapping by correlation between topograghy and geological structure:the Janghung area,Korea. Geomorphology, 46: 149-162. https://doi.org/ 10.1016/S0169-555X(02)00057-0 [DOI:10.1016/S0169-555X(02)00057-0]
135. Menhaj Mohammad Baqer, (2021) Basics of Neural Networks, Publications of Amir Kabir University of Technology (Tehran Polytechnic), 1(11), 715 pages.
136. Mantovani, J. R., G. T. Bueno, E. Alcântara, E. Park, A. P. Cunha, L. Londe, K. Massi, and J. A. Marengo. 2023. “Novel Landslide Susceptibility Mapping Based on Multi-Criteria Decision-Making in Ouro Preto, Brazil.” Journal of Geovisualization and Spatial Analysis 7 (1),71-92.
137. Moghimi, Ibrahim. Ulumbanah, Seyyed Kazem and Jafari, Timur. (2009). Evaluation and zoning of factors affecting the occurrence of landslides in the northern slopes of Aladagh. Case study: Chenaran drainage basin in North Khorasan province, Institute of Geography, University of Tehran, Journal of Geographical Research, 64(9), 53 - 77. [In Persian].
138. Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzymodel to landslid-susceptibility mapping for shallow landslides in a tropical hilly area, Computers & Geosciences, 37(9), 1264-1276.
139. Rajabi, A M., Khosravi, H., 2019 The Zoning of Earthquake-Induced Earthquake Hazards using the AHP Model. Journal of Engineering Geology; 12 (4):635-658.
140. Shadfar, Samad, 2016, investigation of factors affecting landslide and its zoning using GIS in Peltan watershed, 3rd Conference of Spatial Information Systems, Qeshm, (In Persian).
141. https://civilica.com/doc/10889
142. Shirani, K., Naderi Samani, R. (2022). 'Determination of Effective factors and Assessment of Landslide Susceptibility Using Random Forest and Artificial Neural Network in Doab Samsami Region, Chaharmahal va Bakhtiari Province', Watershed Management Research Journal, 35(1), 40-60,(In Persian). https://doi.org/ 10.22092/wmrj.2021.354962.1421 [DOI:10.22092/wmrj.2021.354962.1421]
143. Xu, C., Xu, X., Dai, F., & Saraf, A.K. (2012). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 wenchuan earthquake in china. Computers & Geosciences, 46, 317-329.
144. Yalcin, A., Reis, S., Aydinoglu, A., & Yomralioglu, 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, 85(3), 274-287.
145. 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 and Geosciences, 35: 1125 – 1138.
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