Assessing the risk of check dam failure due to heavy rainfall using machine learning on the Loess Plateau,China  

作　　者：Yulan Chen Jianjun Li Juying Jiao Leichao Bai Nan Wang Tongde Chen Ziqi Zhang Qian Xu jianqiao Han 

机构地区：[1]The Research Center of Soil and Water Conservation and Ecological Environment,Chinese Academy of Sciences and Ministry of Education,Yangling,Shaanxi,712100,China [2]Institute of Soil and Water Conservation,Chinese Academy of Sciences and Ministry of Water Resources,Yangling,Shanxi,712100,China [3]University of Chinese Academy of Sciences,Beijing,100049,China [4]Institute of Soil and Water Conservation,Northwest A&F University,Yangling,Shaanxi,712100,China [5]School of Geographical Sciences,China West Normal University,Nanchong,637009,China [6]School of Politics and Public Administration,Qinghai Minzu University,Xining,Qinghai,810007,China

出　　处：《International Soil and Water Conservation Research》2024年第3期506-520,共15页国际水土保持研究（英文）

基　　金：financial support was provided by National Natural Science Foundation of China(U2243213);Jianqiao Han reports financial support was provided by National Natural Science Foundation of China(42177327);Jianqiao Han reports financial support was provided by National Natural Science Foundation of China(U2243212).

摘　　要：Check dams are widely used throughout the world to tackle soil and water lOSS.However,the ffequency of extreme rainfall events has increased owing to global climate change and the main structure of check dam is gradually aging.which lead to an increase in the failure risk ofcheck dams.Thus.it is necessary to carry out the study on failure risk diagnosis and assessment of check dams.In the study,machine learning algorithms(ML).including random forests(RF).support vector machine(SVM),and logistic regression(LR).were used to integrate the environmental and engineering factors and then assess the risk of check dam failure due to the“7.26”rainstorm on Iuly 26.2017,in the Chabagou watershed.10cated in the hilly—gully region of the Loess Plateau.China.To veri~the generalizability of the model in this study。these models were used for the Wangmaogou catchment north of the Loess Plateau.The accuracy assessment by the receiver operating Characteristic fROCl curve indicated that the RF model with an area under the ROC curve fAUCl greater than 0.89 was the most precise model and had a higher general—ization ability.In addition.the model dataset was relatively smalI and easy to obtain.which make the risk modeling of check dam failure in the study has the potential for application in other regions.In the RF model.each factor selected was confirmed to be important,and the importance values for engineering factors were generally higher than those for the environmental factors.The risk map of check dam failure in the RF modelindicated that 56.34%of check dams in the study area had very high and high risks of dam failure under high—intensity rainfall in 2017.Based on the importance of factors and the risk map of check dam failure.the prevention and control measures for reducing the risk of check dam failure and promoting the construction of check dam are proposed.These proposals provide a scientific basis for the reinforcement of check dams and the future layout of check dams in the Chinese Loess Plateau.

关 键 词：Check dam Failure risk Machine learning Reinforcement measures Loess Plateau 

分 类 号：S157[农业科学—土壤学] TP181[农业科学—农业基础科学]