Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis | |
Yang, Lianbing3,4,5; Ge, Yonggang4,5; Chen, Baili2,3; Wu, Yuhong3,4; Fu, Runde1,3 | |
刊名 | WATER |
2024-04-01 | |
卷号 | 16期号:7页码:22 |
关键词 | machine learning debris flow occurrence prediction meta-analysis |
ISSN号 | 2073-4441 |
DOI | 10.3390/w16070923 |
英文摘要 | Machine learning (ML) has become increasingly popular in the prediction of debris flow occurrence, but the various ML models utilized as baseline predictors reported in previous studies are typically limited to individual case bases. A comprehensive and systematic evaluation of existing empirical evidence on the utilization of ML as baseline predictors for debris flow occurrence is lacking. To address this gap, we conducted a meta-analysis of ML-based prediction modeling of debris flow occurrence by retrieving papers that were published between 2000 and 2023 from the Scopus and Web of Science databases. The general findings were as follows: (1) A total of 84 papers, distributed across 37 different journals in this time period, reflecting an overall upward trend. (2) Debris flow disasters occur throughout the world, and a total of 13 countries carried out research on the prediction of debris flow occurrence based on ML; China made significant contributions, but more research efforts in African countries should be considered. (3) A total of 36 categories of ML models were utilized as baseline predictors for debris flow occurrence, with logistic regression (LR) and random forest (RF) emerging as the most popular choices. (4) Feature engineering and model comparison were the most commonly utilized strategies in predicting debris flow occurrence based on ML (53 and 46 papers, respectively). (5) Interpretation methods were rarely utilized in predicting debris flow occurrence based on ML, with only 16 papers reporting their utilization. (6) In the prediction of debris flow occurrence based on ML, interpretation methods were rarely utilized, searching by data materials was the most important sample data source, the topographic factors were the most commonly utilized category of candidate variables, and the area under the ROC curve (AUROC) was the most frequently reported evaluation metric. (7) LR's prediction performance for debris flow occurrence was inferior to that of RF, BPNN, and SVM; SVM was comparable to RF, and all superior to BPNN. (8) The application process for the prediction of debris flow occurrence based on ML consisted of three main steps: data preparation, model construction and evaluation, and prediction outcomes. The research gaps in predicting debris flow occurrence based on ML include utilizing new ML techniques and enhancing the interpretability of ML. Consequently, this study contributes both to academic ML research and to practical applications in the prediction of debris flow occurrence. |
资助项目 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP) |
WOS关键词 | SUSCEPTIBILITY ; LANDSLIDES ; IMPACTS ; HAZARDS ; CHINA |
WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:001201031100001 |
资助机构 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP) |
内容类型 | 期刊论文 |
源URL | [http://ir.imde.ac.cn/handle/131551/57992] |
专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
通讯作者 | Ge, Yonggang |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610299, Peoples R China 5.Chinese Acad Sci, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610299, Peoples R China |
推荐引用方式 GB/T 7714 | Yang, Lianbing,Ge, Yonggang,Chen, Baili,et al. Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis[J]. WATER,2024,16(7):22. |
APA | Yang, Lianbing,Ge, Yonggang,Chen, Baili,Wu, Yuhong,&Fu, Runde.(2024).Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis.WATER,16(7),22. |
MLA | Yang, Lianbing,et al."Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis".WATER 16.7(2024):22. |
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