Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method
Assessing Landslide Susceptibility Mapping (LSM) contributes to reducing the risk of living with landslides. Handling the vagueness associated with LSM is a challenging task. Here we show the application of hybrid GIS-based LSM. The hybrid approach embraces fuzzy membership functions (FMFs) in combi...
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| Format: | Article |
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MDPI AG
2016-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/18/10/343 |
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| author | Majid Shadman Roodposhti Jagannath Aryal Himan Shahabi Taher Safarrad |
| author_facet | Majid Shadman Roodposhti Jagannath Aryal Himan Shahabi Taher Safarrad |
| author_sort | Majid Shadman Roodposhti |
| collection | DOAJ |
| description | Assessing Landslide Susceptibility Mapping (LSM) contributes to reducing the risk of living with landslides. Handling the vagueness associated with LSM is a challenging task. Here we show the application of hybrid GIS-based LSM. The hybrid approach embraces fuzzy membership functions (FMFs) in combination with Shannon entropy, a well-known information theory-based method. Nine landslide-related criteria, along with an inventory of landslides containing 108 recent and historic landslide points, are used to prepare a susceptibility map. A random split into training (≈70%) and testing (≈30%) samples are used for training and validation of the LSM model. The study area—Izeh—is located in the Khuzestan province of Iran, a highly susceptible landslide zone. The performance of the hybrid method is evaluated using receiver operating characteristics (ROC) curves in combination with area under the curve (AUC). The performance of the proposed hybrid method with AUC of 0.934 is superior to multi-criteria evaluation approaches using a subjective scheme in this research in comparison with a previous study using the same dataset through extended fuzzy multi-criteria evaluation with AUC value of 0.894, and was built on the basis of decision makers’ evaluation in the same study area. |
| first_indexed | 2024-04-12T19:54:47Z |
| format | Article |
| id | doaj.art-c76e31c32f534a948bbcb50dd84a44ef |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-12T19:54:47Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-c76e31c32f534a948bbcb50dd84a44ef2022-12-22T03:18:41ZengMDPI AGEntropy1099-43002016-09-01181034310.3390/e18100343e18100343Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping MethodMajid Shadman Roodposhti0Jagannath Aryal1Himan Shahabi2Taher Safarrad3Discipline of Geography and Spatial Sciences, School of Land & Food, University of Tasmania, Hobart 7001, AustraliaDiscipline of Geography and Spatial Sciences, School of Land & Food, University of Tasmania, Hobart 7001, AustraliaDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, IranDepartment of Geography and Urban Planning, Faculty of Humanities and Social Science, University of Mazandaran, Babolsar 47416-13534, IranAssessing Landslide Susceptibility Mapping (LSM) contributes to reducing the risk of living with landslides. Handling the vagueness associated with LSM is a challenging task. Here we show the application of hybrid GIS-based LSM. The hybrid approach embraces fuzzy membership functions (FMFs) in combination with Shannon entropy, a well-known information theory-based method. Nine landslide-related criteria, along with an inventory of landslides containing 108 recent and historic landslide points, are used to prepare a susceptibility map. A random split into training (≈70%) and testing (≈30%) samples are used for training and validation of the LSM model. The study area—Izeh—is located in the Khuzestan province of Iran, a highly susceptible landslide zone. The performance of the hybrid method is evaluated using receiver operating characteristics (ROC) curves in combination with area under the curve (AUC). The performance of the proposed hybrid method with AUC of 0.934 is superior to multi-criteria evaluation approaches using a subjective scheme in this research in comparison with a previous study using the same dataset through extended fuzzy multi-criteria evaluation with AUC value of 0.894, and was built on the basis of decision makers’ evaluation in the same study area.http://www.mdpi.com/1099-4300/18/10/343Shannon entropyfuzzy membership function (FMF)landslide susceptibility mapping (LSM)Izeh |
| spellingShingle | Majid Shadman Roodposhti Jagannath Aryal Himan Shahabi Taher Safarrad Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method Entropy Shannon entropy fuzzy membership function (FMF) landslide susceptibility mapping (LSM) Izeh |
| title | Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method |
| title_full | Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method |
| title_fullStr | Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method |
| title_full_unstemmed | Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method |
| title_short | Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method |
| title_sort | fuzzy shannon entropy a hybrid gis based landslide susceptibility mapping method |
| topic | Shannon entropy fuzzy membership function (FMF) landslide susceptibility mapping (LSM) Izeh |
| url | http://www.mdpi.com/1099-4300/18/10/343 |
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