Document Type : پژوهشی
Authors
1 Professor of Natural Engineering Department, Faculty of Natural Resources and Environment, Malayer University
2 Doctor of Watershed Management, Faculty of Natural Resources, Yazd University
Abstract
In this researchlandslide sensitivity was zoned using statistical models intheKurdistan Dam watershed and the most appropriatemodel was introduced.First, the studied area was determined and with field observations, the number of 9 landslides was recorded and a landslide distribution map was prepared. In the next step, the factors affecting the occurrence of landslides including geology, rainfall, land use, distance from the river, distance from the fault, slope and height were identified and then a map of these factors was prepared. To determine the rateof each of the effective factors in the occurrence of landslides, the map of each information layer of the effective factors is integrated with the distribution map of the landslide and using AHP, BWM and FUCOM statistical modelsseparate information layers are weighted and By overlapping different layers, the final landsliderisk zoning mapswere prepared and compared.The results showed that land use in AHP and BWM methods and rainfall lines, in addition to land use inFUCOM method have the greatest effect and the criteria of heightdistance from the fault and slope respectively in the three AHP, BWM and FUCOM methods have the least effect on the occurrence of landslidestheresults showed that the lithological variable has a great role on the occurrenceof landslides in the studied area.In generalthe results showed that in AHP and BWM methods, the numberof required pairwise comparisons increases significantly with the number of compared parameters, and in this case, the uncertaintyof opinions increases, which shows the superiority of the FUCOM method over It showswell in other ways.
Keywords
Main Subjects
Abedini, M., Ranjbari, A., & Mokhtari, D. (2020). Landslide risk analysis using ANP and LR models in GIS environment (Case study of Ghoshadagh-Arasbaran fault zone in East Azerbaijan), Quantitative Geomorphological Research Paper, 8(1), 70-88. [in persian].
Akbari, M., Meshram, S.G., Krishna, R., Pradhan, B., Shadeed, S., Khedher,KM., Sepehri, M., Ildoromi, A.R., Alimerzaei, F., & Darabi, F. (2021). Identification of the groundwater potential recharge zones usingMCDM models: full consistency method (FUCOM), best worstmethod (BWM) and analytic hierarchy process (AHP), Water Resour Manage, 35(14):4727–4745. [in persian].
Asghari Saraskanroud, S., Palizban, D., Emami, H., & Qala, A. (2021). Evaluation of Fuzzy Logic and Network Analysis Models for Mapping Landslide Sensitivity Case Study: (Sarab - Nir Road), Geography and Planning Journal, 24(73), 1-22. [in persian].
Badi, I., & Abdulshahed, A. (2019). Ranking the Libyan airlines by using full consistency method (FUCOM) and analytical hierarchy process (AHP). Operational Research in Engineering Sciences, Theory and Applications, 2(1), 1-14. [in persian].
Balbenta, MJI., Capistrano, ADP., David, JT., Tenaja, HT., Poso, FD., & Solomon, MB. (2021), Generation of flood hazard maps in MarikinaCity using GIS-MCDA interval rough AHP (IR’AHP). In: IEEE13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment,and Management (HNICEM).
Basu, T., & Pal, S. (2019). RS-GIS based morphometrical and geological multi-criteria approach to the landslide susceptibility mapping in Gish River Basin, West Bengal, India. Advances in Space Research, 63(3), 1253-1269.
Bera, S., Guru, B., & Ramesh, V. (2019). Evaluation of landslide susceptibility models: A comparative study on the part of Western Ghat Region, India. Remote Sensing Applications: Society and Environment, 13, 39-52.
Broeckx, J., Vanmaercke, M., Duchateau, R, & Poesen, J. (2018). A data-based landslide susceptibility map of Africa, Earth-Science Reviews, 185, 102-121.
Bakhtiari, M.; Gomeh, Z., & Memarian, H. (2019). Comparison of three methods of fuzzy hierarchical analysis process, artificial neural network and surface density in quantitative evaluation and landslide sensitivity zoning in GIS environment (Case study: Seymareh Homian watershed), Journal of Geography and Environmental Hazards, 7(27), 19 to 40 [in persian].
Costanzo, D., E. Rotigliano, C. Irigaray, J. D. Jimenez-Pervarez, & Chacon, J. (2012). Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain), Nat Hazards Earth Syst Sci, 12, 327-340.
Hong, H., Pradhan, B., Jebur, M.N., Bui, D.T., Xu, C., & Akgun, A. (2016). Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines, Environmental Earth Sciences, 75(1), 40-52.
Hijazi, A., Rezaei Moghadam, M., H., & Naseri, A. (2020). Landslide hazard zoning using artificial neural network models and TOPSIS downstream of Sanandaj Dam, Hydrogeomorphology Journal, 7(24), 65-82. [in persian].
Ildoromi, A., Nouri, H., Mohammadipour, M., & Mousavi, M. (2018). Investigation of effective factors and landslide risk zoning using surface density model, hierarchical analysis (AHP) and logistic regression in Ashvand watershed, Environmental Erosion Research, 7,(28), 1-23. [in persian].
Ildoromi, A., Ebadi, F. (2021). Evaluation of efficiency of landslide hazard zoning models of Kurdistan dam watershed, Journal of Quantitative Geomorphology Research, 10(2), 64-83. [in persian].
Jenifer, M. A., & Jha, M. K. (2017). Comparison of Analytic Hierarchy Process, Catastrophe and Entropy techniques for evaluating groundwater prospect of hard-rock aquifer systems, Journal of Hydrology, 548, 605-624.
Khan, H., Shafique, M., Khan, A., Mian, A., Bacha, S., & Chiara, C. (2018). Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan, The Egyptian, Journal of Remote Sensing and Space Sciences, 10 (16), 103-104. [in persian].
Maghsoudi, M., Mohammad Khan, Sh., Pirani, P., Riahi, S. (1397). Investigation of Factors Affecting Landslide Risk Upstream of Latian Dam Using Entropy and Fuzzy Assessment Methods, Journal of Geography and Environmental Hazards, 28, 1-17 . [in persian].
Najafi Igdir, A., Rustai, S., Hijazi, S., A., Rajabi, M., & Jalali, N. (2021). The use of two-variable statistical models in landslide risk zoning in the Nazlochai catchment, Hydrogeomorphology Journal, 8(27), 1-17. [in persian].
Najafi Igdir, A., Rustai, (2019). Prioritization of affecting factors on the landslide occurrence using the logistic regression model (Case study: Nazlochai basin), Hydrogeomorphology Journal, 7(23), 59-81. [in persian].
Nojavan, m., Shah Zaidi, S., Davoodi, M., & Amin Raaya, H. (2020). Landslide risk zoning using a combination of two models of hierarchical and fuzzy analysis process (Case study: Kameh watershed, Isfahan province), Journal of Quantitative Geomorphology Research, 7(4), 142-159. [in persian].
Pamucar, D., Stevic, Z., & Sremac, S. (2018). A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM). Symmetry, 10, 393.
Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert Systems with Applications, 91, 89-106.
Pamučar, D., Lukovac, V., Božanić, D., Komazec, N. (2018). Multi-criteria FUCOM-MAIRCA model for the evaluation of level crossings: Case study in the Republic of Serbia. Oper. Res. Eng. Sci. Theory Appl, 1, 108–129.
Pamučar, D., Stević, Z., & Sremac, S. (2018), A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM), 10(9), 393.
Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Syst. Appl, 42, 9152–9164. [in persian].
Rezaei, J. (2016), Best-worst multi-criteria decision-making method: Some properties and a linear model, 64, 126–130. [in persian].
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. [in persian].
Rezaei, J. (2020). A Concentration Ratio for Non-Linear Best Worst Method. International Journal of Information Technology & Decision Making, 19(3), 891-907. [in persian].
Rostaei, S., Mokhtari, D., & Ashrafi Fini, Z. (2019). Landslide hazard zonation in Taleghan watershed using Shannon entropy index, Journal of Geography and Planning, 24(71), 125- 150. [in persian].
Teymouri, M., & Asadi Nalivan, O. (2020), Susceptibility Zoning and Prioritization of the Factors Affecting Landslide Using MaxEnt, Geographic Information System and Remote Sensing Models (Case study: Lorestan Province), Hydrogeomorphology, 6(21), 155-179. [in persian].
Tian, Z. P., Wang, J. Q., & Zhang, H. Y. (2018). An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing, 72, 636-646.
Yousefi, H., & Yarahmadi, Y. (2020). Landslide risk assessment using a combined model of network analysis (ANP) and fuzzy logic (Case study: Salavat Abad Basin, Sanandaj), Journal of Echo Hydrology, 6 (4): 993-1002. [in persian].
Zhao, H., Yao, L., Mei, G., Liu, T., & Ning, Y. (2017). A fuzzy comprehensive evaluation method based on AHP and entropy for a landslide susceptibility map. Entropy, 19(8), 396.
)