气候变化领域集成服务门户(CCPortal): Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach

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DOI	10.1007/s11069-020-04438-2
	Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach
	Adnan R.M.; Petroselli A.; Heddam S.; Santos C.A.G.; Kisi O.
发表日期	2021
ISSN	0921030X
起始页码	2987
结束页码	3011
卷号	105 期号:3
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.
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
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206066
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
推荐引用方式 GB/T 7714	Adnan R.M.,Petroselli A.,Heddam S.,等. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach[J],2021,105(3).
APA	Adnan R.M.,Petroselli A.,Heddam S.,Santos C.A.G.,&Kisi O..(2021).Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach.Natural Hazards,105(3).
MLA	Adnan R.M.,et al."Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach".Natural Hazards 105.3(2021).