Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector
This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved developing a landslide risk map using hazard and vulnera...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/13/2625 |
| _version_ | 1797527898406518784 |
|---|---|
| author | Yue Wang Haijia Wen Deliang Sun Yuechen Li |
| author_facet | Yue Wang Haijia Wen Deliang Sun Yuechen Li |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved developing a landslide risk map using hazard and vulnerability maps utilizing landslide dataset from 2001 to 2016. The landslide dataset was built from historical records, satellite images and extensive field surveys. Firstly, under four primary conditioning factors (i.e., topographic factors, geological factors, meteorological and hydrological factors and vegetation factors), 19 dominant factors were selected from 25 secondary conditioning factors based on the GeoDetector to form an evaluation factor library for the LSM. Subsequently, the random forest model (RF) was used to analyze landslide susceptibility. Then, the landslide hazard map was generated based on the landslide susceptibility mapping (LSM) for the study region. Thereafter, landslide vulnerability assessment was conducted using key elements (economic, material, community) and the weights were provided based on expert judgment. Finally, when risk equals vulnerability multiplied by hazard, the region was categorized as very low, low, medium, high and very high risk level. The results showed that most landslides distribute on both sides of the reservoir bank and the primary and secondary tributaries in the study area, which showed a spatial distribution pattern of more north than south. Elevation, lithology and groundwater type are the main factors affecting landslides. Fengjie County landslide risk level is mostly low (accounting for 73.71% of the study area), but a small part is high and very high risk level (accounting for 2.5%). The overall risk level shows the spatial distribution characteristics of high risk in the central and eastern urban areas and low risk in the southern and northern high-altitude areas. Secondly, it is necessary to strictly control the key risk areas, and carry out prevention and control zoning management according to local conditions. The study is conducted for a specific region but can be extended to other areas around the investigated area. The developed landslide risk map can be considered by relevant government officials for the smooth implementation of management at the regional scale. |
| first_indexed | 2024-03-10T09:50:24Z |
| format | Article |
| id | doaj.art-efe2c0ac5fe24bdeb554e55d4de7c40a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:50:24Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-efe2c0ac5fe24bdeb554e55d4de7c40a2023-11-22T02:50:04ZengMDPI AGRemote Sensing2072-42922021-07-011313262510.3390/rs13132625Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetectorYue Wang0Haijia Wen1Deliang Sun2Yuechen Li3Chongqing Engineering Research Center for Application of Remote Sensing Big Data, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400045, ChinaKey Laboratory of GIS Application in Chongqing University, Chongqing 401331, ChinaChongqing Engineering Research Center for Application of Remote Sensing Big Data, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaThis study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved developing a landslide risk map using hazard and vulnerability maps utilizing landslide dataset from 2001 to 2016. The landslide dataset was built from historical records, satellite images and extensive field surveys. Firstly, under four primary conditioning factors (i.e., topographic factors, geological factors, meteorological and hydrological factors and vegetation factors), 19 dominant factors were selected from 25 secondary conditioning factors based on the GeoDetector to form an evaluation factor library for the LSM. Subsequently, the random forest model (RF) was used to analyze landslide susceptibility. Then, the landslide hazard map was generated based on the landslide susceptibility mapping (LSM) for the study region. Thereafter, landslide vulnerability assessment was conducted using key elements (economic, material, community) and the weights were provided based on expert judgment. Finally, when risk equals vulnerability multiplied by hazard, the region was categorized as very low, low, medium, high and very high risk level. The results showed that most landslides distribute on both sides of the reservoir bank and the primary and secondary tributaries in the study area, which showed a spatial distribution pattern of more north than south. Elevation, lithology and groundwater type are the main factors affecting landslides. Fengjie County landslide risk level is mostly low (accounting for 73.71% of the study area), but a small part is high and very high risk level (accounting for 2.5%). The overall risk level shows the spatial distribution characteristics of high risk in the central and eastern urban areas and low risk in the southern and northern high-altitude areas. Secondly, it is necessary to strictly control the key risk areas, and carry out prevention and control zoning management according to local conditions. The study is conducted for a specific region but can be extended to other areas around the investigated area. The developed landslide risk map can be considered by relevant government officials for the smooth implementation of management at the regional scale.https://www.mdpi.com/2072-4292/13/13/2625landslidesusceptibility assessmentrandom forestGeoDetectorrisk assessment |
| spellingShingle | Yue Wang Haijia Wen Deliang Sun Yuechen Li Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector Remote Sensing landslide susceptibility assessment random forest GeoDetector risk assessment |
| title | Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector |
| title_full | Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector |
| title_fullStr | Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector |
| title_full_unstemmed | Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector |
| title_short | Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector |
| title_sort | quantitative assessment of landslide risk based on susceptibility mapping using random forest and geodetector |
| topic | landslide susceptibility assessment random forest GeoDetector risk assessment |
| url | https://www.mdpi.com/2072-4292/13/13/2625 |
| work_keys_str_mv | AT yuewang quantitativeassessmentoflandslideriskbasedonsusceptibilitymappingusingrandomforestandgeodetector AT haijiawen quantitativeassessmentoflandslideriskbasedonsusceptibilitymappingusingrandomforestandgeodetector AT deliangsun quantitativeassessmentoflandslideriskbasedonsusceptibilitymappingusingrandomforestandgeodetector AT yuechenli quantitativeassessmentoflandslideriskbasedonsusceptibilitymappingusingrandomforestandgeodetector |