Short- and long-term streamflow forecasting using wavelet neural networks for complex watersheds: A case study in the Mahanadi River, India

Abstract

Water plans and operations (e.g., flood control, drought mitigation measures, water allocation, and engineering design) depend on reliable streamflow information. Thus, this study presents a methodology that improves streamflow forecasting using wavelet neural networks (WNN) for the short- (daily) and long-term (weekly, fortnightly, and monthly) in the Mahanadi River basin, India. The WNN model employs multilayer artificial neural networks (NN) to relate streamflows with wavelet-based approximations of previous streamflows and rainfalls. The methodology was validated using data from ten stations and three performance indices: Pearson correlation coefficient (R), percentage of trends (PBIAS), and Nash-Sutcliffe efficiency (NSE). These indices confirmed that adding rainfall data as input provided better estimations than the sole use of streamflows. The WNN approach was superior to all other applications (NSE ranging from 0.299 to 0.987 for all time horizons and stations), especially for long-term forecasts in the Mahanadi River basin, and could be a viable alternative to other catchments.