Short–long-term streamflow forecasting using a coupled wavelet transform–artificial neural network (WT–ANN) model at the Gilgit River Basin, Pakistan

Abstract

Abstract Streamflow forecasting is highly crucial in the domain of water resources. For this study, we coupled the Wavelet Transform (WT) and Artificial Neural Network (ANN) to forecast Gilgit streamflow at short-term (T0.33 and T0.66), intermediate-term (T1), and long-term (T2, T4, and T8) monthly intervals. Streamflow forecasts are uncertain due to stochastic disturbances caused by variations in snow-melting routines and local orography. To remedy this situation, decomposition by WT was undertaken to enhance the associative relation between the input and target sets for ANN to process. For ANN modeling, cross-correlation was used to guide input selection. Corresponding to six intervals, nine configurations were developed. Short-term intervals performed best, especially for T0.33; intermediate intervals showed decreasing performance. However, interestingly, performance regains back to a decent level for long-term forecasting. Almost all the models underestimate high flows and slightly overestimate low- to intermediate-flow conditions. At last, inference implicitly implies that shorter forecasting benefits from extrapolated trends, while the good results of long-term forecasting is associated to a larger recurrent pattern of the Gilgit River. In this way, weak performance for intermediate forecasting could be attributed to the insufficient ability of the model to capture either one of these patterns.