Rainfall-runoff modeling at Jinsha River basin by integrated neural network with discrete wavelet transform

Artificial neural network (ANN) models combined with time series decomposition are widely employed to calculate the river flows; however, the influence of the application of diverse decomposing approaches on assessing correctness is inadequately compared and examined. This study investigates the certainty of monthly streamflow by applying ANNs including feed forward back propagation neural network and radial basis function neural network (RBFNN) models integrated with discrete wavelet transform (DWT), at Jinsha River basin in the upper reaches of Yangtze River of China. The effect of the noise factor of the decomposed time series on the prediction correctness has also been argued in this paper. Data have been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors, mean absolute errors, mean absolute percentage error and Nash-Sutcliffe Efficiency. Results show that time series decomposition technique DWT contributes in improving the accuracy of streamflow prediction, as compared to single ANN's. The detailed comparative analysis showed that the RBFNN integrated with DWT has better forecasting capabilities as compared to other developed models. Moreover, for high-precision streamflow prediction, the high-frequency section of the original time series is very crucial, which is understandable in flood season.