Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques
Landslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic...
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Taylor & Francis Group
2019-04-01
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| Series: | Annals of GIS |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2019.1575906 |
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| author | Harjeet Kaur Srimanta Gupta Surya Parkash Raju Thapa Arindam Gupta G. C. Khanal |
| author_facet | Harjeet Kaur Srimanta Gupta Surya Parkash Raju Thapa Arindam Gupta G. C. Khanal |
| author_sort | Harjeet Kaur |
| collection | DOAJ |
| description | Landslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. Landslide inventories are prepared based on LISS-IV (MX) satellite imagery, Google Earth and reported data of Geological Survey of India. Altogether 12 landslide conditioning factors viz. slope, elevation, curvature, aspect, land use/land cover, geology, lineament, rainfall, soil type, soil thickness, water regime and distance from road are considered as input data for geospatial modelling of landslide susceptibility. Finally, model-derived landslide susceptibility maps are classified into four hazard zones, i.e. low, medium, high and very high. To measure model compatibility model comparison is performed in ArcGIS environment and models performance is assessed by confusion matrix where RF-FL gives more accuracy of 69.36% than other two models with 9.68% and 19.35% of Type I and type II error, respectively. The outputs are validated using success and prediction rate method where, RF-PLR, RF-FL and RF-IOE show area under curve (AUC) of success and prediction rate as 76%, 67%, 83%, 78% and 85%, 80%, respectively. Additionally, the differences in model performances were analyzed by means of Wilcoxon signed rank test, where it was found that statistically differences in the performance was significant in case of RF-PLR vs. RF-FL and RF-PLR vs. RF-IOE. |
| format | Article |
| id | doaj-art-1a74390ac9f241c5b98e97a216e21a0c |
| institution | Kabale University |
| issn | 1947-5683 1947-5691 |
| language | English |
| publishDate | 2019-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of GIS |
| spelling | doaj-art-1a74390ac9f241c5b98e97a216e21a0c2024-12-17T09:59:30ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912019-04-0125211313210.1080/19475683.2019.1575906Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniquesHarjeet Kaur0Srimanta Gupta1Surya Parkash2Raju Thapa3Arindam Gupta4G. C. Khanal5Department of Environmental Science, The University of Burdwan, West Bengal, IndiaDepartment of Environmental Science, The University of Burdwan, West Bengal, IndiaHydro-Meteorological Division, National Institute of Disaster Management, New Delhi, IndiaDepartment of Environmental Science, The University of Burdwan, West Bengal, IndiaDepartment of Statistics, The University of Burdwan, West Bengal, IndiaLand Revenue & Disaster Management Department, Sikkim State Disaster Management Authority, Gangtok, IndiaLandslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. Landslide inventories are prepared based on LISS-IV (MX) satellite imagery, Google Earth and reported data of Geological Survey of India. Altogether 12 landslide conditioning factors viz. slope, elevation, curvature, aspect, land use/land cover, geology, lineament, rainfall, soil type, soil thickness, water regime and distance from road are considered as input data for geospatial modelling of landslide susceptibility. Finally, model-derived landslide susceptibility maps are classified into four hazard zones, i.e. low, medium, high and very high. To measure model compatibility model comparison is performed in ArcGIS environment and models performance is assessed by confusion matrix where RF-FL gives more accuracy of 69.36% than other two models with 9.68% and 19.35% of Type I and type II error, respectively. The outputs are validated using success and prediction rate method where, RF-PLR, RF-FL and RF-IOE show area under curve (AUC) of success and prediction rate as 76%, 67%, 83%, 78% and 85%, 80%, respectively. Additionally, the differences in model performances were analyzed by means of Wilcoxon signed rank test, where it was found that statistically differences in the performance was significant in case of RF-PLR vs. RF-FL and RF-PLR vs. RF-IOE.https://www.tandfonline.com/doi/10.1080/19475683.2019.1575906Landslide susceptibilityGangtokSikkim Himalaya |
| spellingShingle | Harjeet Kaur Srimanta Gupta Surya Parkash Raju Thapa Arindam Gupta G. C. Khanal Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques Annals of GIS Landslide susceptibility Gangtok Sikkim Himalaya |
| title | Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques |
| title_full | Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques |
| title_fullStr | Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques |
| title_full_unstemmed | Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques |
| title_short | Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques |
| title_sort | evaluation of landslide susceptibility in a hill city of sikkim himalaya with the perspective of hybrid modelling techniques |
| topic | Landslide susceptibility Gangtok Sikkim Himalaya |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2019.1575906 |
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