Forecast of total flows for medium-term horizon via data-driven modeling
DOI:
https://doi.org/10.5902/2179460X87856Keywords:
Inflow forecast, Machine learning, Weka, Reservoir operationAbstract
This study presents the application of artificial neural networks (ANN), k-nearest neighbors algorithm (KNN), and support vector regression (SVR) for modeling the prediction of inflows to the Sobradinho reservoir in Bahia, Brazil. Using the Weka software, six formulations were tested for 3-month forecasts, with data divided into cross-validation and calibration-validation schemes. Efficiency was evaluated using the Nash-Sutcliffe coefficient, highlighting the superiority of SVR. The inclusion of attributes such as average flow and precipitation improved efficiencies. The model using KNN with 13 neighbors was incorporated into an enhanced implicit stochastic optimization strategy for the operation of the reservoir. This model was compared to other operational methods, showing superiority in vulnerability index.
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