Performance evaluation of artificial neural network model in hybrids with various preprocessors for river streamflow forecasting

Abstract

Abstract Accurate forecasting of hydrological processes and sustainable management of water resources is inevitable, especially for flood control and water resource shortage crisis in low-water areas with an arid and semi-arid climate, which is a limitation for residents and various structures. The present study uses different data preprocessing techniques to deal with complex data and extract hidden features from the stream time series. In the next step, the decomposed time series were used, as input data, to the artificial neural network (ANN) model for streamflow modeling and forecasting. The preprocessors employed included discrete wavelet transform (DWT), empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), successive variational mode decomposition (SVMD), and multi-filter of the smoothing (MFS). These preprocessors were used in hybrid with the ANN model to forecast the daily streamflow. In general, the results showed that the optimal performance of hybrid models has two basic steps. The first step is choosing a suitable approach to utilizing the input data to the model. The second step is to use the appropriate preprocessor. Overall, the results show that the MFS-ANN model in short-term forecasting and the SVMD-ANN model in long-term forecasting performed better than other hybrid models.