Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction

Abstract

A correct estimation of the instantaneous peak flow (IPF) is crucial to reducing the consequences of flash floods. An approach to estimate the IPF, obtained by combining Soil and Water Assessment Tool (SWAT) simulation and machine-learning models, was proposed and then verified by comparison with observation-based results in the Ladra river basin, northwest Spain. The SWAT model has been used to estimate the maximum mean daily flow (MMDF), and machine-learning models have been used to estimate the IPF based on MMDF. Four nonlinear time-series intelligence models, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and extreme learning machine (ELM) were applied, and their results were compared. The Modified Nash-Sutcliffe efficiency coefficient (MNSE) and the index of agreement (d) were used to evaluate SWAT performance while simulating MMDF, and the coefficient of determination (R2) and the root mean square error (RMSE) were employed to evaluate the performance of these intelligent systems. According to the results, the SWAT hydrological model is a useful tool to simulate MMDF. Validation analyses resulted in values of statistical indexes (MNSE = 0.64 and d = 0.95). Regarding intelligent systems, the results show that they can be successfully used in predicting IPF, but ELM has demonstrated a superior ability to estimate IPF from the MMDF (R2 = 0.86 and RMSE = 48.59). The results of this study can contribute to predicting IPF in areas where sub-daily observational data are scarce, thereby reducing uncertainties associated with IPF estimations.