globalchange  > 气候变化与战略
DOI: 10.1016/j.earscirev.2020.103225
论文题名:
Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
作者: Merghadi A.; Yunus A.P.; Dou J.; Whiteley J.; ThaiPham B.; Bui D.T.; Avtar R.; Abderrahmane B.
刊名: Earth Science Reviews
ISSN: 00128252
出版年: 2020
卷: 207
语种: 英语
中文关键词: Landslide ; Machine learning ; Natural hazard ; Random forest ; Susceptibility
英文关键词: algorithm ; comparative study ; geological mapping ; GIS ; landslide ; machine learning ; performance assessment ; Algeria
英文摘要: Landslides are one of the catastrophic natural hazards that occur in mountainous areas, leading to loss of life, damage to properties, and economic disruption. Landslide susceptibility models prepared in a Geographic Information System (GIS) integrated environment can be key for formulating disaster prevention measures and mitigating future risk. The accuracy and precision of susceptibility models is evolving rapidly from opinion-driven models and statistical learning toward increased use of machine learning techniques. Critical reviews on opinion-driven models and statistical learning in landslide susceptibility mapping have been published, but an overview of current machine learning models for landslide susceptibility studies, including background information on their operation, implementation, and performance is currently lacking. Here, we present an overview of the most popular machine learning techniques available for landslide susceptibility studies. We find that only a handful of researchers use machine learning techniques in landslide susceptibility mapping studies. Therefore, we present the architecture of various Machine Learning (ML) algorithms in plain language, so as to be understandable to a broad range of geoscientists. Furthermore, a comprehensive study comparing the performance of various ML algorithms is absent from the current literature, making an assessment of comparative performance and predictive capabilities difficult. We therefore undertake an extensive analysis and comparison between different ML techniques using a case study from Algeria. We summarize and discuss the algorithm's accuracies, advantages and limitations using a range of evaluation criteria. We note that tree-based ensemble algorithms achieve excellent results compared to other machine learning algorithms and that the Random Forest algorithm offers robust performance for accurate landslide susceptibility mapping with only a small number of adjustments required before training the model. © 2020 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166183
Appears in Collections:气候变化与战略

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作者单位: Research Laboratory of Sedimentary Environment, Mineral and Water resources of Eastern Algeria, Larbi Tébessi UniversityTebessa, Algeria; State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, China; Three Gorges Research Center for Geo-Hazards, Ministry of Education, China University of Geosciences, Wuhan, 430074, China; Department of Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka, Japan; School of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, Bristol, BS8 1RJ, United Kingdom; British Geological Survey, Environmental Science Centre, Nicker Hill, Keyworth, Nottingham, NG12 5GG, United Kingdom; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; GIS group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, 3800 Bø i Telemark, Norway; Faculty of Environmental Earth Science, Hokkaido University, Sapporo, 060-0810, Japan

Recommended Citation:
Merghadi A.,Yunus A.P.,Dou J.,et al. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance[J]. Earth Science Reviews,2020-01-01,207
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