Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods

This paper investigates the potential of using data-driven methods, namely Bayesian neural networks (BNN), recurrent multi-layer perceptrons (RMLP), time-lagged feed-forward networks (TLFN), and conventional multi-layer perceptrons (MLP) to forecast seasonal reservoir inflows of the Churchill Falls watershed in northeastern Canada. A climate variability indicator (the El Niño-Southern Oscillation, ENSO) is used as additional information to historical inflow time series in order to predict seasonal reservoir inflows. The prediction results showed that the Bayesian neural network model was best able to capture the additional information provided by the ENSO series, and provided improved predictions in spring and summer seasons relative to the same model using only reservoir inflows. Similarly, time-lagged feed-forward networks and recurrent multi-layer perceptrons showed some improved forecast skill in spring when the ENSO index series are used but generally provided superior performance overall. The conventional multi-layer perceptron appears unable to capture relevant information from the ENSO series regardless of the season. However, when only historical flow series are used, all the selected data-driven methods provide very competitive forecast performances.