Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms | |
Wu, Hao1,2,3; Nian, Tingkai1; Shan, Zhigang2 | |
刊名 | GEOMATICS NATURAL HAZARDS & RISK |
2023-12-31 | |
卷号 | 14期号:1页码:20 |
关键词 | Landslide dam life span prediction machine learning algorithms database landslide dam type |
ISSN号 | 1947-5705 |
DOI | 10.1080/19475705.2023.2273213 |
英文摘要 | A rapid and accurate prediction of a landslide dam's life span is of significant importance for emergency geological treatment. However, current prediction models for the state of a landslide dam are based solely on geomorphological indexes, and do not take into consideration attribute properties such as landslide types, trigger factors, and dam types. This study investigates the relationships between a landslide dam's geometry and the capacity of the barrier lake and proposes fitting models, which supplement the current landslide dam database. Subsequently, six predictive models for landslide dam life span are established, utilizing machine learning algorithms such as logistic regression, k-nearest neighbors, support vector machine, Naive Bayes, decision tree, and random forest, which consider five factors, including geometry parameters and attribute properties. The performances of these six models are analyzed and compared to a typical prediction model, the dimensionless blockage index (DBI). The results suggest that the models established in this study not only have a consistent absolute accuracy as the DBI model, but also overcome the disadvantage that a large number of cases cannot be judged by the DBI model. Among the formulated machine learning models, the random forest model exhibits the highest absolute accuracy (89%), lowest error rate (7%), lowest false alarm rate (15%), and no uncertainty rate. Additionally, three renowned landslide dams, namely the Costantino, Hsiaolin, and Baige landslide dams, are analyzed to illustrate the applicability of the established machine learning models. The study results provide essential guidance for the predictions and emergency geological treatments of landslide dam disasters. |
资助项目 | Critical comments by anonymous reviewers greatly improved the initial manuscript. Thanks to Associate Professor Yihuai Lou of Zhejiang University for his valuable suggestions on this paper. |
WOS关键词 | HSIAOLIN VILLAGE ; TREE ; CLASSIFICATION ; MOUNTAINS ; STABILITY ; FAILURE ; TAIWAN |
WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences ; Water Resources |
语种 | 英语 |
出版者 | TAYLOR & FRANCIS LTD |
WOS记录号 | WOS:001087544500001 |
资助机构 | Critical comments by anonymous reviewers greatly improved the initial manuscript. Thanks to Associate Professor Yihuai Lou of Zhejiang University for his valuable suggestions on this paper. |
内容类型 | 期刊论文 |
源URL | [http://ir.imde.ac.cn/handle/131551/57683] |
专题 | 中国科学院水利部成都山地灾害与环境研究所 |
通讯作者 | Nian, Tingkai |
作者单位 | 1.Dalian Univ Technol, Sch Civil Engn, Dalian, Liaoning, Peoples R China 2.POWERCHINA Huadong Engn Corp Ltd, Hangzhou, Zhejiang, Peoples R China 3.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Sichuan, Peoples R China |
推荐引用方式 GB/T 7714 | Wu, Hao,Nian, Tingkai,Shan, Zhigang. Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms[J]. GEOMATICS NATURAL HAZARDS & RISK,2023,14(1):20. |
APA | Wu, Hao,Nian, Tingkai,&Shan, Zhigang.(2023).Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms.GEOMATICS NATURAL HAZARDS & RISK,14(1),20. |
MLA | Wu, Hao,et al."Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms".GEOMATICS NATURAL HAZARDS & RISK 14.1(2023):20. |
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