Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks

Abstract

In semi-arid watersheds, hydrological drought is manifested by reasonably low streamflow conditions. This makes streamflow forecasting as an inevitable component for implementing drought management practices. Data-driven modelling techniques are often applied for simulating the streamflow forecasts. In this study, a comparison between conventional feedforward neural network (FFNN) model and wavelet enabled artificial neural network (WANN) model is carried out to analyse their effectiveness in streamflow forecasting. The input data used to develop and simulate the models are monthly precipitation, and monthly river stage of twenty-five years (January 1991 to December 2015). Data pre-processing is carried out using correlation analysis prior to neural network modelling for selecting appropriate input combinations. The preprocessed data is directly given as input for FFNN; whereas for WANN, the preprocessed time series datasets are decomposed into several sub-series and are used as the inputs. Analysis on three different transfer functions that are commonly used in ANN models is carried out to identify the best transfer function. Hyperbolic tangent sigmoid transfer function is found to be best suitable for modelling streamflow forecasts. The result also shows that there is a significant improvement in streamflow forecasting ability for WANN models compared to FFNN. Drought forecasting is carried out by developing a standardized streamflow index from the forecasted streamflow. The drought forecasting technique discussed here will help planners to make informed decisions on watershed management and drought mitigation measures.