Geomechanics and Engineering

  • 제45권3호
  • /
  • Pages.293-314
  • /
  • 2026
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Enhanced mapping of rainfall induced landslide susceptibility using a deep feedforward neural network with soft computing

  • Licai Zhu (School of Artificial Intelligence, Yancheng Teachers University) ;
  • Tolga Pusatli (Department of Management Information Systems, Cankaya University) ;
  • Amila Akagic (Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo) ;
  • Dong Jian (School of Artificial Intelligence, Yancheng Teachers University) ;
  • Elkhan Mahmud (International School of Economics, Azerbaijan State University of Economics) ;
  • Yaser A. Nanehkaran (School of Artificial Intelligence, Yancheng Teachers University)
  • 투고 : 2025.04.17
  • 심사 : 2026.04.30
  • 발행 : 2026.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

The presented study attempted to propose enhanced rainfall-induced landslide susceptibility mapping method by using the Deep Feedforward Neural Network (DFNN) which is developed for analysis the non-liner feature detection in landslide susceptibility analysis. To evaluate our approach, a comprehensive dataset of triggering factors was compiled, encompassing historical landslide occurrences with total of 107 records, rainfall data, geological information, seismicity, human-activities, and topographic attributes. Through rigorous training and testing procedures, the DFNN demonstratedsuperior ability for generalization and superior performance. The effectiveness of the selected method is demonstrated on the data from the Zanjan County, known for its diverse geographical, geological, and hydrological characteristics, which are pivotal factors in mapping of landslide susceptibility. Results showcased a substantial enhancement in the accuracy of mapping of rainfall-induced landslide susceptibility for the Zanjan County, which is compared with benchmark learning classifiers. According to the results of the study, it appeared that the northeastern and southwestern area of the Zanjan County can be deemed to have a high to very-high risk of landslide occurrence, which is validated via benchmark classifiers. The western part of the Zanjan County was observed to have a very low to low risk.

키워드

과제정보

The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper. The manuscript is conducted in an ethical manner as advised by the targeted journal. This work is a collaboration of researchers from Chinese, Bosnian, Turkish, and Azerbaijani universities. The scholars are happy to have this as a product of our newly founded Artificial Intelligence Research Laboratory of Yancheng Wetland (AIRLYW) under the supervision of Prof. Dr. Zhu Licai. The researchers appreciate deeply from government of China in providing the required facilities to implement and complete the research plan. This research was funded by the National Natural Sciences Foundation of China under grant No. of 42250410321. All data are available within the article.

참고문헌

  1. O'Rourke, T.D. (2010). Geohazards and large, geographically distributed systems. Géotechnique, 60(7), 505-543. https://doi.org/10.1680/geot.2010.60.7.505.
  2. Luat, N.V., Do, T.N., Nguyen, L.C., Kien, N.T. (2024). Assessing landslide susceptibility along the Halong-Vandon expressway in Quang Ninh province, Vietnam: A comprehensive approach integrating GIS and various methods. Geomechanics and Engineering, 37(2), 135-147. https://doi.org/10.12989/gae.2024.37.2.135.
  3. Tiwari, S., Das, S.K., Mohanty, M. (2024). Improved prediction of soil liquefaction susceptibility using ensemble learning algorithms. Geomechanics and Engineering, 37(5), 475-498. https://doi.org/10.12989/gae.2024.37.5.475.
  4. Merghadi, A., Yunus, A.P., Dou, J., Whiteley, J., Pham, B.T., Bui, D.T., Avtar, R., Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. https://doi.org/10.1016/j.earscirev.2020.103225.
  5. Mao, Y., Mwakapesa, D.S., Wang, G., Nanehkaran, Y.A., Zhang, M. (2021). Landslide susceptibility modelling based on AHC-OLID clustering algorithm. Advances in Space Research, 68(1), 301-316. https://doi.org/10.1016/j.asr.2021.03.014.
  6. Mao, Y., Li, Y., Mwakapesa, D.S., Wang, G., Nanehkaran, Y.A., Khan, M.A., Zhang, M. (2021). Innovative landslide susceptibility mapping portrayed by CA-AQD and K-means clustering algorithms. Advances in Civil Engineering, 2021(1), 8846779. https://doi.org/10.1155/2021/8846779.
  7. Nanehkaran, Y.A., Licai, Z., Chen, J., Azarafza, M., Mao, Y. (2022). Application of artificial neural networks and geographic information system to provide hazard susceptibility maps for rockfall failures. Environmental Earth Sciences, 81, 475. https://doi.org/10.1007/s12665-022-10603-6.
  8. Tehrani, F.S., Calvello, M., Liu, Z., Zhang, L., Lacasse, S. (2022). Machine learning and landslide studies: Recent advances and applications. Natural Hazards, 114, 1197-1245. https://doi.org/10.1007/s11069-022-05423-7.
  9. Chen, W., Li, Y. (2020). GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena, 195, 104777. https://doi.org/10.1016/j.catena.2020.104777.
  10. Shano, L., Raghuvanshi, T.K., Meten, M. (2020). Landslide susceptibility evaluation and hazard zonation techniques: a review. Geoenvironmental Disasters, 7, 18. https://doi.org/10.1186/s40677-020-00152-0.
  11. Ngo, P.T.T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Kariminejad, N., Cerdà, A., Lee, S. (2021). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2), 505-519. https://doi.org/10.1016/j.gsf.2020.06.013.
  12. Mao, Y., Mwakapesa, D.S., Li, Y., Xu, K., Nanehkaran, Y.A., Zhang, M. (2022). Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods. Journal of Mountain Science, 19(1), 184-197. https://doi.org/10.1007/s11629-020-6491-7.
  13. Mikoš, M., Sassa, K., Han, Q. (2023). International programme on landslides—a short overview of its historical development. (Eds., Sassa, K., Konagai, K., Tiwari, B., Arbanas, Ž., Sassa, S.), Progress in Landslide Research and Technology, 1, 45-62. Cham: Springer International Publishing.
  14. Ghiasi, V., Pauzi, N.I.M., Karimi, S., Yousefi, M. (2023). Landslide risk zoning using support vector machine algorithm. Geomechanics and Engineering, 34(3), 267-284. https://doi.org/10.12989/gae.2023.34.3.267.
  15. Pourkhosravani, M., Mehrabi, A., Pirasteh, S., Derakhshani, R. (2022). Monitoring of Maskun landslide and determining its quantitative relationship to different climatic conditions using D-InSAR and PSI techniques. Geomatics, Natural Hazards and Risk, 13(1), 1134-1153. https://doi.org/10.1080/19475705.2022.2065939.
  16. Azarafza, M., Ghazifard, A., Akgün, H., Asghari-Kaljahi, E. (2018). Landslide susceptibility assessment of South Pars special zone, southwest Iran. Environmental Earth Sciences, 77, 805. https://doi.org/10.1007/s12665-018-7978-1.
  17. 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.
  18. Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P.M., Derakhshani, R. (2021). Deep learning-based landslide susceptibility mapping. Scientific Reports, 11, 24112. https://doi.org/10.1038/s41598-021-03585-1.
  19. Huang, F., Cao, Z., Jiang, S.H., Zhou, C., Huang, J., Guo, Z. (2020). Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides, 17, 2919-2930. https://doi.org/10.1007/s10346-020-01473-9.
  20. Pourghasemi, H.R., Rahmati, O. (2018). Prediction of landslide susceptibility: Which algorithm, which precision?. Catena, 162, 177-192. https://doi.org/10.1016/j.catena.2017.11.022.
  21. Mao, Y., Chen, L., Nanehkaran, Y.A., Azarafza, M., Derakhshani, R. (2023). Fuzzy-based intelligent model for rapid rock slope stability analysis using Qslope. Water, 15(16), 2949. https://doi.org/10.3390/w15162949.
  22. Mao, Y., Li, Y., Teng, F., Sabonchi, A.K.S., Azarafza, M., Zhang, M. (2024). Utilizing hybrid machine learning and soft computing techniques for landslide susceptibility mapping in a drainage basin. Water, 16(3), 380. https://doi.org/10.3390/w16030380.
  23. Birjandi, D., Derakhshani, R. (2025). Landslide hazard assessment induced by active tectonics in the Golpayegan region using AHP. Civil and Geoengineering Letters, 2(2), e100043. https://doi.org/10.22034/CGEL.2.2.e100043.
  24. Abgrami, A., Zhang, W., Mao, H., Wang, L. (2025). GIS-based comparative landslide susceptibility mapping for Kelardasht county with ANN, SVM and RF models. Civil and Geoengineering Letters, 2(1), e100028. https://doi.org/10.22034/CGEL.2.1.e100028.
  25. Cordoba, J.P., Mergili, M., Aristizábal, E. (2020). Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model. Natural Hazards and Earth System Sciences, 20(3), 815-829. https://doi.org/10.5194/nhess-20-815-2020.
  26. 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. https://doi.org/10.1016/j.earscirev.2018.03.001.
  27. Van Westen, C.J., Castellanos, E., Kuriakose, S.L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Engineering Geology, 102(3-4), 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010.
  28. 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, 139-140, 545-558. https://doi.org/10.1016/j.geomorph.2011.12.006.
  29. Nikoobakht, S., Azarafza, M., Akgün, H., Derakhshani, R. (2022). Landslide susceptibility assessment by using convolutional neural network. Applied Sciences, 12(12), 5992. https://doi.org/10.3390/app12125992.
  30. Arabameri, A., Pradhan, B., Rezaei, K., Lee, C.W. (2019). Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sensing, 11(9), 999. https://doi.org/10.3390/rs11090999.
  31. Ermini, L., Catani, F., Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4), 327-343. https://doi.org/10.1016/j.geomorph.2004.09.025.
  32. Lei, X., Chen, W., Pham, B.T. (2020). Performance evaluation of GIS-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS International Journal of Geo-Information, 9(7), 443. https://doi.org/10.3390/ijgi9070443.
  33. Pradhan, B., Dikshit, A., Lee, S., Kim, H. (2023). An explainable AI (XAI) model for landslide susceptibility modeling. Applied Soft Computing, 142, 110324. https://doi.org/10.1016/j.asoc.2023.110324.
  34. Wang, Y., Fang, Z., Wang, M., Peng, L., Hong, H. (2020). Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers & Geosciences, 138, 104445. https://doi.org/10.1016/j.cageo.2020.104445.
  35. Wang, H., Zhang, L., Luo, H., He, J., Cheung, R.W.M. (2021). AI-powered landslide susceptibility assessment in Hong Kong. Engineering Geology, 288, 106103. https://doi.org/10.1016/j.enggeo.2021.106103.
  36. Bui, D.T., Tsangaratos, P., Nguyen, V.T., Liem, N.V., Trinh, P.T. (2020). Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188, 104426. https://doi.org/10.1016/j.catena.2019.104426.
  37. He, K., Ma, G., Hu, X., Liu, B., Han, M. (2022). The July 2, 2017, Lantian landslide in Leibo, China: Mechanisms and mitigation measures. Geomechanics and Engineering, 28(3), 283-298. https://doi.org/10.12989/gae.2022.28.3.283.
  38. Nanehkaran, Y.A., Licai, Z., Chengyong, J., Chen, J., Anwar, S., Azarafza, M., Derakhshani, R. (2023). Comparative analysis for slope stability by using machine learning methods. Applied Sciences, 13(3), 1555. https://doi.org/10.3390/app13031555.
  39. Kim, D., Ko, J., Kim, J. (2023). Application of UAV images for rainfall-induced slope stability analysis in urban areas. Geomechanics and Engineering, 33(2), 167-174. https://doi.org/10.12989/gae.2023.33.2.167.
  40. Ikram, R.M.A., Khan, I., Moayedi, H., Dehrashid, A.A., Elkhrachy, I., Le, B.N. (2024). Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environment, Development and Sustainability, 26, 17687-17719. https://doi.org/10.1007/s10668-023-03356-0.
  41. Rahimi, F., Ebrahimi, M., Mehraban, R.F. (2025). Assessment of landslide susceptibility using fuzzy logic: A case study of Nur County. Civil and Geoengineering Letters, 2(2), e100046. https://doi.org/10.22034/CGEL.2.2.e100046.
  42. Ebadati, M., Sun, F., Lee, Y., Moghadam, M.J. (2025). Landslide susceptibility analysis using artificial neural networks for Chalus County, Iran. Civil and Geoengineering Letters, 2(2), e100039. https://doi.org/10.22034/CGEL.2.2.e100039.
  43. Ghorbani, M. (2013). The economic geology of Iran. Dordrecht: Springer.
  44. Geological Survey of Iran (GSI). (2009). Geological map and report of Zanjan County. Tehran, Iran: GSI Map and Report Department.
  45. Thanh, D.Q., Nguyen, D.H., Prakash, I., Jaafari, A., Nguyen, V.T., Phong, T.V., Pham, B.T. (2020). GIS-based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam. Vietnam Journal of Earth Sciences, 42(1), 55-66. https://doi.org/10.15625/0866-7187/42/1/14758.
  46. Derakhshani, R., Zaresefat, M., Nikpeyman, V., GhasemiNejad, A., Shafieibafti, S., Rashidi, A., Raoof, A. (2023). Machine learning-based assessment of watershed morphometry in Makran. Land, 12(4), 776. https://doi.org/10.3390/land12040776.
  47. Ghanbarian, M.A., Derakhshani, R. (2022). The folds and faults kinematic association in Zagros. Scientific Reports, 12(1), 8350. https://doi.org/10.1038/s41598-022-12337-8.
  48. Ghanbarian, M.A., Yassaghi, A., Derakhshani, R. (2021). Detecting a sinistral transpressional deformation belt in the Zagros. Geosciences, 11(6), 226. https://doi.org/10.3390/geosciences11060226.
  49. Highland, L.M., Bobrowsky, P. (2008). The landslide handbook: A guide to understanding landslides. Reston, Virginia: USGS Circular 1325.
  50. Cemiloglu, A., Zhu, L., Mohammednour, A.B., Azarafza, M., Nanehkaran, Y.A. (2023). Landslide susceptibility assessment for Maragheh County, Iran, using the logistic regression algorithm. Land, 12(7), 1397. https://doi.org/10.3390/land12071397.
  51. Das, I., Stein, A., Kerle, N., Dadhwal, V.K. (2012). Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology, 179, 116-125. https://doi.org/10.1016/j.geomorph.2012.08.004.
  52. Sahana, M., Pham, B.T., Shukla, M., Costache, R., Thu, D.X., Chakrabortty, R., Satyam, N., Nguyen, H.D., Phong, T.V., Le, H.V., Pal, S.C., Areendran, G., Imdad, K., Prakash, I. (2022). Rainfall-induced landslide susceptibility mapping using novel hybrid soft computing methods based on multilayer perceptron neural network classifier. Geocarto International, 37(10), 2747-2771. https://doi.org/10.1080/10106049.2020.1837262.
  53. Varnes, D.J. (1978). Slope movement types and processes. In: Landslides: Analysis and Control (TRB Special Report 176, 11-33. Washington, DC: Transportation Research Board.
  54. Pham, B.T., Prakash, I., Jaafari, A., Bui, D.T. (2018). Spatial prediction of rainfall-induced landslides using aggregating one-dependence estimators classifier. Journal of the Indian Society of Remote Sensing, 46(9), 1457-1470. https://doi.org/10.1007/s12524-018-0791-1.
  55. Dehghananari, M. (2025). Seismic hazard mapping of southern Kerman using GIS: integrating active geostructural features. Civil and Geoengineering Letters, 2(2), e100038. https://doi.org/10.22034/CGEL.2.2.e100038.
  56. Tsaparas, I., Rahardjo, H., Toll, D.G., Leong, E.C. (2002). Controlling parameters for rainfall-induced landslides. Computers and Geotechnics, 29(1), 1-27. https://doi.org/10.1016/S0266-352X(01)00019-2.
  57. Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., Alamri, A.M. (2020). Rainfall-induced landslide studies in Indian Himalayan region: A critical review. Applied Sciences, 10(7), 2466. https://doi.org/10.3390/app10072466.
  58. Iran Meteorological Organization (IMO) (2023). Zanjan rainfall annual data (Zanjan station). Retrieved from http://www.irimo.ir
  59. Vamvatsikos, D., Nigro, E., Kouris, L.A., Panagopoulos, G., Kappos, A., Rossetto, T., Lloyd, T.O., Stathopoulos, T. (2010). Performance assessment under multiple hazards. (Ed., Mazzolani, F.M.), Urban habitat constructions under catastrophic events, 271-289. London: CRC Press.
  60. Zhao, P., Masoumi, Z., Kalantari, M., Aflaki, M., Mansourian, A. (2022). A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods. Remote Sensing, 14(1), 211. https://doi.org/10.3390/rs14010211.
  61. Abedini, M., Nesaz, H.S. (2014). Application of analytical hierarchy process (AHP) in landslide hazard zoning occurrence: A case study of Gollojeh watershed. Journal of Geographic Planning, 18(49), 139-165.
  62. Shoaei, Z., Emamjomeh, R. (2020). Prediction of time to failure in creep type large-scale landslide. Ecopersia, 8(1), 1-14.
  63. Aggarwal, C.C. (2018). Neural networks and deep learning: a textbook. Berlin: Springer.
  64. Chollet, F. (2017). Deep learning with Python. New York, NY: Manning Publications.