Deep Learning-Based Salinity Forecasting in the Vietnamese Mekong Delta: A Cung Hau Estuary Case Study

Salinity intrusion has become a critical threat to agricultural stability and water resource
management in the Vietnamese Mekong Delta (VMD), particularly in coastal regions. This
study evaluates the efficacy of the Long Short-Term Memory (LSTM) neural network, a
sophisticated deep learning (DL) architecture, for predicting salinity concentrations at two
monitoring stations: Hung My and Tra Vinh. Using historical salinity data, the research
explores the impact of varying the lookback window from 15- to 45-day and the forecast
horizons (1- to 3-day) on model performance. Experimental results demonstrate that the
15-day lookback window provides the most robust temporal context, enabling the model
to achieve high predictive accuracy for short-term horizons. For 1-day forecast horizon, the
model achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.85 and low Root Mean
Square Error (RMSE) at both stations. However, a progressive decline in performance was
observed as the lead time extended to 3-day forecast horizon, primarily due to increased
prediction uncertainty and the inherent non-linearity of estuarine dynamics. A detailed
analysis of the results reveals a consistent underestimation of extreme salinity peaks, a
phenomenon attributed to the smoothing effect of the Mean Squared Error (MSE) loss
function and the absence of real-time exogenous inputs such as wind speed and tidal
pressure. These findings provide a valuable scientific foundation for developing early
warning systems, offering actionable insights for farmers and supporting evidence-based
decision-making for policymakers in managing salinity risks.