[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: 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:   (538 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
Full-Text [PDF 1642 kb]   (235 Downloads)    
Type of Study: Research | Subject: GIS
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Darehshuri M A, Yazdi M. Landslide Susceptibility Areas Detection Using GIS Information and Combinatiom of Machine Learning Models and Meta Heuristic Algorithms. JGST 2022; 12 (1) :111-125
URL: http://jgst.issge.ir/article-1-1063-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 12, Issue 1 (9-2022) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology