Volume 11, Issue 4 (2-2025)
Journal of Spatial Analysis Environmental Hazards 2025, 11(4): 0-0 |
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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-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3453-en.html
1- Assistant Prof, Soil Conservation and Watershed Management Research Department, Ilam Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran. , Shamsasgari@yahoo.com
2- Associate Prof, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
2- Associate Prof, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
Abstract: (739 Views)
Landslides are one of the natural hazards that threaten human life and property. A landslide may destroy tens, hundreds and maybe thousands of hectares of land in a short time. For years, this hazard has destroyed orchard lands, fields, forest areas and pastures, communication roads, and rural settlements in the Mishkhas mountainous region of Ilam province. Landslide risk zoning is necessary to control this risk in this basin. The main goal of this research is the zoning of landslide risk areas in this watershed. One of the new methods to investigate the risk of landslides is the artificial neural network method. This method has advantages over other methods, the statistical distribution of the data is independent and does not require special statistical variables. In this research, first, a landslide distribution map was prepared in the selected basin. Then, the relationship between independent variables such as slope, lithology, distance from fault, land use, distance from road network, distance from waterways, direction of slope with areas affected by landslides was investigated. After preparing the weighted maps, these layers were converted into numerical information in the ArcGIS software environment, and after standardization, they were entered into the MATLAB software, and a program with a perceptron structure was written with the learning algorithm after the error propagation. After determining the structure of the artificial neural network and its training and testing, the evaluated results and the output of the network in the geographic information systems environment became a landslide risk map. The resulting risk map was calculated into different risk zones, classification and amount of landslide in each zone. The results of the analysis of the factors showed that in the Mishkhas basin of Ilam, Asmari Formation, the slope is 10-20%, the distance from the fault is more than 500 meters, the northeast direction, the distance from waterways is more than 100 meters, fruit orchards are the most sensitive land uses and the distance from the road is more than 200 meters are the most sensitive classes to the occurrence of landslides and have the highest rate of occurrence of landslides in the basin. On the other hand, the results of landslide risk zoning using artificial neural network method showed that in Mishkhas Basin of Ilam, about 80% of landslides are in high and very high-risk zones.
Type of Study: Research |
Subject:
Special
Received: 2024/06/20 | Accepted: 2025/03/11 | Published: 2025/03/11
Received: 2024/06/20 | Accepted: 2025/03/11 | Published: 2025/03/11
References
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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 [DOI: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.
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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]. [DOI:10.22059/JPHGR.2009.355408.1007750]
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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. [DOI: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 [DOI: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. [DOI: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. [DOI:10.1016/j.catena.2011.01.014]
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30. 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]
31. 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]
32. 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]
33. 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]
34. 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]
35. 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]
36. 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]
37. 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]
38. 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]
39. 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]
40. 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]
41. 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]
42. 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]
43. 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]
44. 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]
45. 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]
46. 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]
47. 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]
48. Menhaj Mohammad Baqer, (2021) Basics of Neural Networks, Publications of Amir Kabir University of Technology (Tehran Polytechnic), 1(11), 715 pages.
49. 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]
50. 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]
51. 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.
52. 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]
53. 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).
54. https://civilica.com/doc/10889
55. 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]
56. 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]
57. 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]
58. 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]
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