气候变化领域集成服务门户(CCPortal): Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

CCPortal

DOI	10.1016/j.earscirev.2020.103225
	Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
	Merghadi A.; Yunus A.P.; Dou J.; Whiteley J.; ThaiPham B.; Bui D.T.; Avtar R.; Abderrahmane B.
发表日期	2020
ISSN	00128252
卷号	207
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.
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
语种	英语
来源期刊	Earth Science Reviews
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/203814
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,...
推荐引用方式 GB/T 7714	Merghadi A.,Yunus A.P.,Dou J.,et al. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance[J],2020,207.
APA	Merghadi A..,Yunus A.P..,Dou J..,Whiteley J..,ThaiPham B..,...&Abderrahmane B..(2020).Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance.Earth Science Reviews,207.
MLA	Merghadi A.,et al."Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance".Earth Science Reviews 207(2020).