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Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions

文献类型：期刊论文

作者Jiang, Hu4,6; Zou, Qiang5,6; Zhu, Yunqiang2,3; Li, Yong6; Zhou, Bin4,6; Zhou, Wentao4,6; Yao, Shunyu1; Dai, Xiaoliang2,3; Yao, Hongkun4,6; Chen, Siyu6
刊名ENVIRONMENTAL MODELLING & SOFTWARE
出版日期2024-08-01
卷号179页码:22
关键词Spatiotemporal prediction Rainfall-driven debris flow Deep learning Machine learning Rainfall features
ISSN号1364-8152
DOI10.1016/j.envsoft.2024.106130
通讯作者Zou, Qiang(zouqiang@imde.ac.cn)
英文摘要Machine learning has been widely applied to predict the spatial or temporal likelihood of debris flows by leveraging its powerful capability to fit nonlinear features and uncover underlying patterns or rules in the complex formation mechanisms of debris flows. However, traditional approaches, including some current machine learning-based prediction models, still have limitations when used for debris flow prediction. These include the lack of a specific network structure or model to consider the updating of debris flow critical conditions in relation to geographical background conditions, limiting the universality of prediction models when transferring them to different places. In this study, this article proposes a deep learning network designed to predict the spatiotemporal probability of rainfall-induced debris flows, incorporating the Similarity Mechanism of Debris Flow Critical Conditions (SM-DFCC). The model comprehensively integrates the mining of rainfall-triggering features and couples them with geographical background features to fit the nonlinear relationship with debris flow formation. The model underwent training using data on various historical debris flows triggered by different storms across Liangshan Prefecture from 2020 to 2022. The results indicated that: (i) the method is effective in predicting the spatiotemporal likelihood of debris flows under catchment units, with accuracy scores (ACC) ranging from 0.724 to 0.835; (ii) after optimization using the AVOA algorithm, the predictive performance of the model significantly improved, with an increase of 27.24% in ACC scores for SVC and 8.81% for XGBoost; and (iii) factor importance analysis revealed that rainfall triggering factors have higher cumulative contribution rates when distinguishing between the occurrence and non-occurrence of debris flows. In addition, taking a rainfall storm on 06, September 2020 as a case, this research quantitatively revealed the pattern of debris flow formation, where high-frequency disaster areas exhibit lower rainfall thresholds of debris flows, represented by absolute energy (AE). Despite these findings, the accuracy and reliability of rainfall data still remain the most challenging obstacle in basin/regional-scale debris flow prediction when applying this method. The integration of multiple sources of rainfall data, including station data, satellite rainfall, radar rainfall, etc., is necessary to accurately quantify the impact of rainfall on debris flow formation when applying this method to debris flow monitoring and early warning tasks. Overall, this method shows great potential in providing a scientific reference for the construction of debris flow monitoring and early warning systems in the future.
WOS关键词SHALLOW LANDSLIDES ; DURATION CONTROL ; INITIATION ; INTENSITY ; IDENTIFICATION ; UNCERTAINTY ; NETWORKS ; CLIMATE ; IMPACT ; TERM
资助项目Light of West China Program of the Chinese Academy of Sciences[xbzg-zdsys-202104] ; National Nature Science Foundation of China[42171085] ; Key R & D project of Sichuan Provincial Department of Science and Technology[2023YFS0434] ; Research project of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences[IMHE-ZDRW-09]
WOS研究方向Computer Science ; Engineering ; Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:001264364000001
出版者ELSEVIER SCI LTD
资助机构Light of West China Program of the Chinese Academy of Sciences ; National Nature Science Foundation of China ; Key R & D project of Sichuan Provincial Department of Science and Technology ; Research project of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
源URL[http://ir.imde.ac.cn/handle/131551/58180]  
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Zou, Qiang
作者单位1.China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.CAS HEC, China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan
6.Chinese Acad Sci, Inst Mt Hazards & Environm IMHE, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Hu,Zou, Qiang,Zhu, Yunqiang,et al. Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions[J]. ENVIRONMENTAL MODELLING & SOFTWARE,2024,179:22.
APA Jiang, Hu.,Zou, Qiang.,Zhu, Yunqiang.,Li, Yong.,Zhou, Bin.,...&Chen, Siyu.(2024).Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions.ENVIRONMENTAL MODELLING & SOFTWARE,179,22.
MLA Jiang, Hu,et al."Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions".ENVIRONMENTAL MODELLING & SOFTWARE 179(2024):22.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

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