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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.
Cong et al. (Wed,) studied this question.