Applying Macroclimatic Variables To Improve Flow Rate Forecasting Using Neural Networks Techniques

Since 2013, the Sao Francisco River has being through a low hydraulicity period. In other words, the rain intensity is below average. Consequently, it has being necessary to operate at a minimal flow rate. It is far below the ones established at the operation licence, which is 1300 m(3)/s. Due to this hydraulic crisis, the actual operational flow rate is 700 m(3)/s at Sao Francisco River, characterizing this situation as critical. In this work, it was proposed to use Reservoir Computing (RC), Long Short Term Memory (LSTM) and Deep Learning to predict Sobradinho's flow rate for 1, 2 and 3 months ahead using macroclimatic variables. After having the results for each one of them, a comparison was made and statistical tests where executed for evaluation.