Journal
JOURNAL OF HYDROLOGIC ENGINEERING
Volume 24, Issue 2, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)HE.1943-5584.0001725
Keywords
Artificial neural networks (ANN); Discrete wavelet transform (DWT); Forecast; Prediction; Streamflow; Tropical rainfall measuring mission (TRMM)
Funding
- National Council for Scientific and Technological Development, Brazil [304213/2017-9, 304540/2017-0]
- National System Operator, Brazil (ONS)
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES) [001]
- Tropical Rainfall Measuring Mission (TRMM)
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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 Tres Marias, located in the SAo 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%).
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