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

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. [GRAPHICS] .

Automated summary

This study compared the performance of four machine learning methods in hourly rainfall-runoff modeling, demonstrating that the newly developed hybridized MARS-Kmeans method outperformed others in predicting 1-, 6- and 12-hour ahead runoff. In event-based rainfall-runoff modeling, all machine learning models performed better than the conceptual method, with OPELM showing slightly better performance than the other three alternatives.