Conditional Forecasting of Water Level Time Series with Recurrent Neural Networks

Abstract

We describe a practical situation in which the application of forecasting models could lead to energy efficiency and decreased risk in water level management. The practical challenge of forecasting water levels in the next 24 hours and the available data are provided by a dutch regional water authority. We formalized the problem as conditional forecasting of hydrological time series: the resulting models can be used for real-life scenario evaluation and decision support. We propose the novel Encoder/Decoder with Exogenous Variables RNN (ED-RNN) architecture for conditional forecasting with RNNs, and contrast its performance with various other time series forecasting models. We show that the performance of the ED-RNN architecture is comparable to the best performing alternative model (a feedforward ANN for direct forecasting), and more accurately captures short-term fluctuations in the water heights.