Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs

Abstract Monthly streamflow prediction is very important for many hydrological applications in providing information for optimal use of water resources. In this study, the prediction accuracy of new heuristic methods, optimally pruned extreme learning machine (OP-ELM), least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree), is examined in modeling monthly streamflows using precipitation and temperature inputs. Data collected from Kalam and Chakdara stations at a mountainous basin, Swat River Basin, Pakistan are utilized as case study. The prediction accuracy of all four methods are validated and tested using four different input scenarios and evaluated using combined accuracy (CA), a newly used criterion in addition to root-mean-square error (RMSE), normalized RMSE, mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). The test results of both stations show that the LSSVM and MARS-based models provide more accurate prediction results compared to OP-ELM and M5Tree models. LSSVM decreases the RMSE of the MARS, OP-ELM and M5Tree by 9.12%, 25.64% and 35.15% for the Kalam station while the RMSEs of the LSSVM, OP-ELM and M5Tree is decreased by 2.12%, 34.81% and 32.52% using MARS, for the Chakdara Station, respectively. It is observed that the monthly streamflows of Kalam Station can be successfully predicted using only temperature data. Only precipitation inputs also provide good accuracy for Kalam Station while they produce inaccurate predictions for the Chakdara Station. The prediction capabilities of the applied methods are also examined in estimating streamflow of downstream station using upstream data. The results prove the dominancy of LSSVM and MARS-based models over OP-ELM and M5Tree in prediction streamflow data without local input data. Heuristic methods are also compared with stochastic method of seasonal auto regressive moving average (SARIMA). The OP-ELM, LSSVM, MARS perform superior to the SARIMA in monthly streamflow prediction. Based on the overall results, the LSSVM and MARS are recommended for monthly streamflow prediction with or without local data.