Application of Gated Recurrent Unit (GRU) Network for Forecasting River Water Levels Affected by Tides

In light of the proliferation of information technology, the application of deep learning models in the analysis and study of hydrological problems is increasingly becoming common. This paper proposes a new approach using one of the applications of deep learning models to predict river water level in areas where the influence of tides is obvious. The forecasting model is developed based on the recurrent neural network for predicting the water level from one to four time-steps ahead in the downstream of An Tho irrigation culvert on the Luoc River (Vietnam). Each time-step corresponds to the once observed data and the data collected for this study is only the observed water level at the target station - An Tho sluice in over 18 years. Although only a modest amount of data is required, the forecasting model produces superior results. Accuracy in the phase of testing the model is up to 94-96% for all forecasting cases. The findings of this study indicate that the proposed model produces an outstanding performance when the target-forecasting station is clearly affected by the tide. This acts as a precursor of the construction of an operating regime for irrigation sluice gates in the tidal area.