Multi-step-ahead water level forecasting for operating sluice gates in Hai Duong, Vietnam

Recently, machine learning (ML) is being applied to various fields, including hydrology and hydraulics. The numerical models based on ML algorithms have been widely used for forecasting water levels or flowrate in different timescales. Especially in estuary areas where the hydrodynamic regime becomes complicated, the water level forecast information in this area plays an essential role in the operation of tidal sluices. This study proposes an efficient approach using an ML model, long short-term memory (LSTM), to predict short-term water levels in tidal sluice gates from 6 to 48 hours ahead. The An Tho culvert located in the Bac Hung Hai irrigation system, the most extensive irrigation system in Vietnam, was selected as a case study station. The high accuracy of predictive results reveals LSTM models' effectiveness in different forecasting scenarios. In the first scenario using just water level data at the prediction station, the Kling-Gupta efficiency (KGE) coefficient ranges from nearly 0.89 to 0.96. Meanwhile, in the second scenario, the combination of observed data of three gauge stations exhibited better performance with KGE coefficients ranging from just under 0.93 to 0.98 for eight forecasted cases. The findings of this study highlight the performance of LSTM models in providing high-accuracy short-period water level forecasts for areas near estuaries. These obtained results can play a vital role in the management and operation of tidal sluices in the Bac Hung Hai irrigation system, as well as a reference for the operation of other irrigation systems around the world.