Forecasting of sub-surface flow velocity through a temporarily open/close estuary under closed condition using machine learning

TOCE or temporarily open/closed estuary is a complex estuary system created by interaction of longshore transport and river outflows. Under the closed condition when an estuarine sandbar is formed, river flow is impeded creating backwater rise that could result in floods. At present, there are no hydrological models that could cater for river flows under closed estuary conditions. This paper proposes the application of a machine learning (ML) approach to model flow velocity in an estuarine sandbar which is crucial for flow estimations. A TOCE located at Mengabang Telipot, Terengganu, Malaysia was chosen for model development where river water and groundwater levels (inside the sandbar) were measured and tidal levels from an existing station were used. Average flow velocities were calculated using Darcy’s equation to represent observed data. Multilayer perceptron (MLP) neural networks were trained by the Levenberg-Marquardt algorithm and developed concerning the parameters above to predict groundwater average velocity. In this study, the optimal model was a neural network with four neurons in the hidden layer and the degree of influence of each parameter was determined by the results of the sensitivity analysis, where the R 2 was obtained to be about 97–99% when predicted data was compared to observed data. Hence, the results indicated that neural networks were able to predict groundwater mean velocity quite well. The developed model was further verified using data from another TOCE from a different time. The results were very good having a R 2 of 0.990. Hence, it was concluded that the ML approach was able to model flow in a TOCE and has great potential application in such an environment seeing that it is simpler than complex physics-based models.