Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India
Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/10/1711 |
| _version_ | 1797472043195695104 |
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| author | Abhik Saha Vasanta Govind Kumar Villuri Ashutosh Bhardwaj |
| author_facet | Abhik Saha Vasanta Govind Kumar Villuri Ashutosh Bhardwaj |
| author_sort | Abhik Saha |
| collection | DOAJ |
| description | Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on the methodology utilized in the research. An analytical hierarchy process (AHP), a fuzzy-AHP, and an artificial neural network (ANN) are utilized in the current study to construct maps of landslide susceptibility for a part of Darjeeling and Kurseong in West Bengal, India. On a landslide inventory map, 114 landslide sites were randomly split into training and testing with a 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament density, geomorphology, soil texture, land use and land cover, lithology, and rainfall were used as model inputs. The area under the curve (AUC) was used to examine the models. When tested for validation, the ANN prediction model performed best, with an AUC of 88.1%. AUC values for fuzzy-AHP and AHP are 86.1% and 85.4%, respectively. According to the statistics, the northeast and eastern portions of the study area are the most vulnerable. This map might help development in the area by preventing human and economic losses. |
| first_indexed | 2024-03-09T19:57:38Z |
| format | Article |
| id | doaj.art-05ea24f5843c4c9a91f76bae3bc2aa54 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T19:57:38Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-05ea24f5843c4c9a91f76bae3bc2aa542023-11-24T00:52:55ZengMDPI AGLand2073-445X2022-10-011110171110.3390/land11101711Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, IndiaAbhik Saha0Vasanta Govind Kumar Villuri1Ashutosh Bhardwaj2Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, IndiaDepartment of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, IndiaPhotogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248001, IndiaLandslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on the methodology utilized in the research. An analytical hierarchy process (AHP), a fuzzy-AHP, and an artificial neural network (ANN) are utilized in the current study to construct maps of landslide susceptibility for a part of Darjeeling and Kurseong in West Bengal, India. On a landslide inventory map, 114 landslide sites were randomly split into training and testing with a 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament density, geomorphology, soil texture, land use and land cover, lithology, and rainfall were used as model inputs. The area under the curve (AUC) was used to examine the models. When tested for validation, the ANN prediction model performed best, with an AUC of 88.1%. AUC values for fuzzy-AHP and AHP are 86.1% and 85.4%, respectively. According to the statistics, the northeast and eastern portions of the study area are the most vulnerable. This map might help development in the area by preventing human and economic losses.https://www.mdpi.com/2073-445X/11/10/1711landslide susceptibility mappingmulti-criteria decision analysisfuzzy-analytical hierarchy processartificial neural networkDarjeeling Himalayas |
| spellingShingle | Abhik Saha Vasanta Govind Kumar Villuri Ashutosh Bhardwaj Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India Land landslide susceptibility mapping multi-criteria decision analysis fuzzy-analytical hierarchy process artificial neural network Darjeeling Himalayas |
| title | Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India |
| title_full | Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India |
| title_fullStr | Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India |
| title_full_unstemmed | Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India |
| title_short | Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India |
| title_sort | development and assessment of gis based landslide susceptibility mapping models using ann fuzzy ahp and mcda in darjeeling himalayas west bengal india |
| topic | landslide susceptibility mapping multi-criteria decision analysis fuzzy-analytical hierarchy process artificial neural network Darjeeling Himalayas |
| url | https://www.mdpi.com/2073-445X/11/10/1711 |
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