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Abstract:
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Rainfall forecasting is an important decision-making input in a variety of areas, including agriculture, hydropower generation, and water resource planning and management. A reliable forecasting tool would contribute to the reduction of vulnerability and risk in water management systems. However, due to the high spatial-temporal variability of rainfall amounts, it is very difficult to achieve high accuracy in the forecasts. This study addresses the problem of rainfall forecasting by proposing a methodology based on a combination of wavelet decomposition (WD), neural networks (NN), and lagged regression (LR). We implemented WD in a preprocessing phase followed by the use of a recurrent NN algorithm, and proposed a prediction enhancement phase by optimizing the network outputs using a monthly rainfall forecast correction with LR. The methodology was implemented at four weather stations in a tropical region, and it was compared with other powerful forecasting methods. The research results suggest that our approach outperformed other methods in performance accuracy and biases correction, achieving adjusted R squared greater than 0.76 and normalized mean absolute errors less than 0.31
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