:: Volume 12, Issue 1 (9-2022) ::
JGST 2022, 12(1): 111-125 Back to browse issues page
Landslide Susceptibility Areas Detection Using GIS Information and Combinatiom of Machine Learning Models and Meta Heuristic Algorithms
M. A. Darehshuri, M. Yazdi *
Abstract:   (669 Views)
Landslide is a geological phenomenon that occurs in the unstable slopes of mountainous areas and in some cases causes very severe human and economic losses.  Research shows that by using the classification of landslide prone areas, possible future damage can be prevented.  The purpose of this study is to produce a landslide sensitivity map for Ardabil province using two machine learning methods ANFIS and SVM and combining them with PSO and GWO metaheuristic algorithms.  For this purpose, first a landslide map of 253 points was prepared.  Among the slip points, 70% were considered for Training and the remaining 30% were used for validation.  Continuing and according to previous studies and available data, fourteen effective factors including height, slope, slope direction,profile curvature and plan curvature of the slope, land use, lithology, rainfall, distance from the road, distance from the river, distance from the fault, road density , river density and fault density were selected.  After preparing the database using MATLAB software, the combined models SVR - PSO , SVR - GWO , ANFIS - GWO and ANFIS - PSO were implemented and then the landslide sensitivity index was obtained for each model.  During the modeling process, the performance of each method was evaluated using the RMSE statistical index.  Finally, landslide sensitivity maps were generated for each model using ArcMap 10.5 software and then the accuracy of each map was estimated using the ROC curve.  The results show that the ANFIS - psd model is more efficient than the other three models. The results of ROC curve obtained by applying ANFIS - PSO  , ANFIS - GWO , SVR - PSO , SVR - GWO were 89.4, 85.7, 88.1, 88.7,respectively.
Article number: 8
Keywords: Machine Learning, Supervise Learning, Landslide, Optimization Algorithms, GIS
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Type of Study: Research | Subject: GIS

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Volume 12, Issue 1 (9-2022) Back to browse issues page