Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models
Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper c...
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doaj-95ef0c09180f4ac9b91cb0114e6e041d2020-11-25T02:33:23ZengMDPI AGWater2073-44412019-07-01117140210.3390/w11071402w11071402Landslide Susceptibility Mapping Using Different GIS-Based Bivariate ModelsEbrahim Nohani0Meisam Moharrami1Samira Sharafi2Khabat Khosravi3Biswajeet Pradhan4Binh Thai Pham5Saro Lee6Assefa M. Melesse7Young Researchers and Elite Club, Dezful Branch, Islamic Azad University, Dezful 64616-45169, IranDepartment of GIS and RS, Faculty of Geography and Planning, University of Tabriz, Tabriz 51666-16471, IranDepartment of GIS and RS, Faculty of Geography and Planning, University of Tabriz, Tabriz 51666-16471, IranSchool of Engineering, University of Guelph, Guelph, ON N1G 2W1, CanadaFaculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, AustraliaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDivision of Geoscience Research Platform, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahang-no, Yuseong-gu, Daejeon 305-350, KoreaDepartment of Earth and environment, Florida International University, Miami, FL 33174, USALandslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.https://www.mdpi.com/2073-4441/11/7/1402landslidebivariate modelsROCGISKelijanrestaghmolticolinirity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ebrahim Nohani Meisam Moharrami Samira Sharafi Khabat Khosravi Biswajeet Pradhan Binh Thai Pham Saro Lee Assefa M. Melesse |
spellingShingle |
Ebrahim Nohani Meisam Moharrami Samira Sharafi Khabat Khosravi Biswajeet Pradhan Binh Thai Pham Saro Lee Assefa M. Melesse Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models Water landslide bivariate models ROC GIS Kelijanrestagh molticolinirity |
author_facet |
Ebrahim Nohani Meisam Moharrami Samira Sharafi Khabat Khosravi Biswajeet Pradhan Binh Thai Pham Saro Lee Assefa M. Melesse |
author_sort |
Ebrahim Nohani |
title |
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models |
title_short |
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models |
title_full |
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models |
title_fullStr |
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models |
title_full_unstemmed |
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models |
title_sort |
landslide susceptibility mapping using different gis-based bivariate models |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2019-07-01 |
description |
Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area. |
topic |
landslide bivariate models ROC GIS Kelijanrestagh molticolinirity |
url |
https://www.mdpi.com/2073-4441/11/7/1402 |
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