Hybrid Wavelet Neural Network Approach for Daily Inflow Forecasting Using Tropical Rainfall Measuring Mission Data

Abstract

© 2018 American Society of Civil Engineers. A novel wavelet-artificial neural network hybrid model (WA-ANN) for short-term daily inflow forecasting is proposed, using for the first time Tropical Rainfall Measuring Mission (TRMM) data together with inflow data, which were transformed using mother-wavelets to improve the model performance. The models were assessed using the inflow records to a Brazilian reservoir named Três Marias, located in the São Francisco River basin, and daily rainfall estimates from the TRMM both for the period of 1998-2012. Several combinations of inputs for both regular and hybrid artificial neural networks (ANN) were assessed to forecast inflows seven days ahead, and it was proved that the WA-ANN had a superior performance. Even the WA-ANN model, which uses only the approximation at level three of rainfall data, provided a higher performance than the regular ANN, which uses the raw inflow data [r increase 16%, Nash-Sutcliffe model efficiency coefficient (NASH) increase 35%, and root-mean-square deviation (RMSD) decrease 47%]. It was also found the best model was the WA-ANN with transformed rainfall and inflow data as input (r increase 20%, NASH increase 44%, and RMSD decrease 69%).