Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach

Rana Muhammad Adnan (), Andrea Petroselli (), Salim Heddam (), Celso Augusto Guimarães Santos () and Ozgur Kisi ()
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Rana Muhammad Adnan: Hohai University
Andrea Petroselli: University of Tuscia
Salim Heddam: Faculty of Science, Agronomy Department, Hydraulics Division
Celso Augusto Guimarães Santos: Federal University of Paraíba
Ozgur Kisi: Ilia State University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 105, issue 3, No 28, 2987-3011

Abstract: Abstract 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

Keywords: Machine learning; Physically event-based conceptual method; EBA4SUB; Hourly rainfall–runoff modeling (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-020-04438-2

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