Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)
The Sikkim state, including Gangtok, is dominated by Precambrian rocks which contain foliated schists and phyllites; slopes are therefore susceptible to frequent landslides. The recent development of roads and building structures make this region more vulnerable to landslide hazard. In this research...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-12-01
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| Series: | Geology, Ecology, and Landscapes |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24749508.2018.1558024 |
| _version_ | 1819050122764353536 |
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| author | Harjeet Kaur Srimanta Gupta Surya Parkash Raju Thapa |
| author_facet | Harjeet Kaur Srimanta Gupta Surya Parkash Raju Thapa |
| author_sort | Harjeet Kaur |
| collection | DOAJ |
| description | The Sikkim state, including Gangtok, is dominated by Precambrian rocks which contain foliated schists and phyllites; slopes are therefore susceptible to frequent landslides. The recent development of roads and building structures make this region more vulnerable to landslide hazard. In this research work, landslide susceptibility zonation mapping within Gangtok Municipal Corporation (GMC) area have been carried out implementing remote sensing and GIS technique. To derive the landslide susceptibility map (LSM) of GMC, weighted overlay method (WOM) was implemented by assigning weights to various triggering factors via expert opinion. The twelve triggering factors used in the study were geology/lithology, slope morphometry, lineament density, water regime, rainfall, elevation, soil type, soil liquefaction, soil thickness, building density, relative relief, and land use/land covers (LULC). The final LSM of GMC shows that about 19.14% of the study area falls under very high landslide hazard zone and 31.78% area falls under the high category. Medium and low landslide hazard zone encompasses about 30.95% and 18.11 % of the total area, respectively. The model-generated LSM is validated with past reported landslide event where an overall accuracy of above 80% is observed. |
| first_indexed | 2024-12-21T11:43:02Z |
| format | Article |
| id | doaj.art-0932757739ba48de930e76748d723a41 |
| institution | Directory Open Access Journal |
| issn | 2474-9508 |
| language | English |
| last_indexed | 2024-12-21T11:43:02Z |
| publishDate | 2018-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geology, Ecology, and Landscapes |
| spelling | doaj.art-0932757739ba48de930e76748d723a412022-12-21T19:05:15ZengTaylor & Francis GroupGeology, Ecology, and Landscapes2474-95082018-12-010011510.1080/24749508.2018.15580241558024Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)Harjeet Kaur0Srimanta Gupta1Surya Parkash2Raju Thapa3The University of BurdwanThe University of BurdwanNational Institute of Disaster ManagementThe University of BurdwanThe Sikkim state, including Gangtok, is dominated by Precambrian rocks which contain foliated schists and phyllites; slopes are therefore susceptible to frequent landslides. The recent development of roads and building structures make this region more vulnerable to landslide hazard. In this research work, landslide susceptibility zonation mapping within Gangtok Municipal Corporation (GMC) area have been carried out implementing remote sensing and GIS technique. To derive the landslide susceptibility map (LSM) of GMC, weighted overlay method (WOM) was implemented by assigning weights to various triggering factors via expert opinion. The twelve triggering factors used in the study were geology/lithology, slope morphometry, lineament density, water regime, rainfall, elevation, soil type, soil liquefaction, soil thickness, building density, relative relief, and land use/land covers (LULC). The final LSM of GMC shows that about 19.14% of the study area falls under very high landslide hazard zone and 31.78% area falls under the high category. Medium and low landslide hazard zone encompasses about 30.95% and 18.11 % of the total area, respectively. The model-generated LSM is validated with past reported landslide event where an overall accuracy of above 80% is observed.http://dx.doi.org/10.1080/24749508.2018.1558024Landslide susceptibility zonation (LSZ)Gangtok cityknowledge-driven methodvalidation |
| spellingShingle | Harjeet Kaur Srimanta Gupta Surya Parkash Raju Thapa Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ) Geology, Ecology, and Landscapes Landslide susceptibility zonation (LSZ) Gangtok city knowledge-driven method validation |
| title | Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ) |
| title_full | Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ) |
| title_fullStr | Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ) |
| title_full_unstemmed | Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ) |
| title_short | Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ) |
| title_sort | knowledge driven method a tool for landslide susceptibility zonation lsz |
| topic | Landslide susceptibility zonation (LSZ) Gangtok city knowledge-driven method validation |
| url | http://dx.doi.org/10.1080/24749508.2018.1558024 |
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