Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning
ABSTRACT: Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective, diffic...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-07-01
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| Series: | China Geology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096519223014441 |
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| author | Wen-geng Cao Yu Fu Qiu-yao Dong Hai-gang Wang Yu Ren Ze-yan Li Yue-ying Du |
| author_facet | Wen-geng Cao Yu Fu Qiu-yao Dong Hai-gang Wang Yu Ren Ze-yan Li Yue-ying Du |
| author_sort | Wen-geng Cao |
| collection | DOAJ |
| description | ABSTRACT: Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective, difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km2 and 3087.45 km2, accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.©2023 China Geology Editorial Office. |
| format | Article |
| id | doaj-art-05a62dcb24834bc196cd3f84000ccdf2 |
| institution | Kabale University |
| issn | 2589-9430 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | China Geology |
| spelling | doaj-art-05a62dcb24834bc196cd3f84000ccdf22025-08-20T03:33:18ZengKeAi Communications Co., Ltd.China Geology2589-94302023-07-016340941910.31035/cg2023013Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learningWen-geng Cao0Yu Fu1Qiu-yao Dong2Hai-gang Wang3Yu Ren4Ze-yan Li5Yue-ying Du6The Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Science, China Geological Survey, Ministry of Natural Resources, Shijiazhuang 050061, China; Key Laboratory of Groundwater Sciences and Engineering, Ministry of Natural Resources, Shijiazhuang, 050061, P.R. China; First author: E-mail address: caowengeng@mail.cgs.gov.cn (Wen-geng Cao).North China University of Water Resources and Electric Power, Zhengzhou 450011, ChinaThe Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Science, China Geological Survey, Ministry of Natural Resources, Shijiazhuang 050061, China; Corresponding author: E-mail address: dongqiuyao@mail.cgs.gov.cn (Qiu-yao Dong).China Institute of Geo-Environment Monitoring, Beijing 100081, ChinaThe Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Science, China Geological Survey, Ministry of Natural Resources, Shijiazhuang 050061, ChinaThe Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Science, China Geological Survey, Ministry of Natural Resources, Shijiazhuang 050061, ChinaNorth China University of Water Resources and Electric Power, Zhengzhou 450011, ChinaABSTRACT: Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective, difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km2 and 3087.45 km2, accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.©2023 China Geology Editorial Office.http://www.sciencedirect.com/science/article/pii/S2096519223014441Landslide susceptibility modelRisk assessmentMachine learningSupport vector machinesLogistic regressionRandom forest |
| spellingShingle | Wen-geng Cao Yu Fu Qiu-yao Dong Hai-gang Wang Yu Ren Ze-yan Li Yue-ying Du Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning China Geology Landslide susceptibility model Risk assessment Machine learning Support vector machines Logistic regression Random forest |
| title | Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning |
| title_full | Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning |
| title_fullStr | Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning |
| title_full_unstemmed | Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning |
| title_short | Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning |
| title_sort | landslide susceptibility assessment in western henan province based on a comparison of conventional and ensemble machine learning |
| topic | Landslide susceptibility model Risk assessment Machine learning Support vector machines Logistic regression Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2096519223014441 |
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