Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks

Abstract

An experiment on predicting multivariate water resource time series, specifically the prediction of hydropower reservoir inflow using temporal neural networks, is presented. This paper focuses on dynamic neural networks to address the temporal relationships of the hydrological series. Three types of temporal neural network architectures with different inherent representations of temporal information are investigated. An input delayed neural network (IDNN) and a recurrent neural network (RNN) with and without input time delays are proposed for multivariate reservoir inflow forecasting. The forecast results indicate that, overall, the RNN obtained the best performance. The results also suggest that the use of input time delays significantly improves the conventional multilayer perceptron (MLP) network but does not provide any improvement in the RNN model. However, the RNN with input time delays remains slightly more effective for multivariate reservoir inflow prediction than the IDNN model. Moreover, it is found that the conventional MLP network widely used in hydrological applications is less effective at multivariate reservoir inflow forecasting than the proposed models. Furthermore, the experiment shows that employing only time-delayed recurrences can be the more effective and less costly method for multivariate water resources time series prediction.