globalchange  > 气候变化与战略
DOI: 10.1007/s11069-020-04438-2
论文题名:
Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach
作者: Adnan R.M.; Petroselli A.; Heddam S.; Santos C.A.G.; Kisi O.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 105, 期:3
起始页码: 2987
结束页码: 3011
语种: 英语
中文关键词: EBA4SUB ; Hourly rainfall–runoff modeling ; Machine learning ; Physically event-based conceptual method
英文关键词: comparative study ; conceptual framework ; data set ; error analysis ; flood control ; machine learning ; model validation ; rainfall-runoff modeling ; regression analysis ; watershed ; Germany ; Ilme River
英文摘要: Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall–runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swapping training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall–runoff modeling. Graphic abstract: [Figure not available: see fulltext.]. © 2021, Springer Nature B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168865
Appears in Collections:气候变化与战略

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作者单位: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098, China; Department of Economy, Engineering, Society and Business (DEIM), University of Tuscia, Viterbo, Italy; Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria; Department of Civil and Environmental Engineering, Federal University of Paraíba, Paraíba, Brazil; Civil Engineering Department, Ilia State University, Tbilisi, Georgia; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

Recommended Citation:
Adnan R.M.,Petroselli A.,Heddam S.,et al. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach[J]. Natural Hazards,2021-01-01,105(3)
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