Salinity forecasting in the Vietnamese Mekong Delta: Evaluating the predictive power of machine learning approaches using multitemporal lag features - Le, Van Quyen; Nguyen, Tuan Cuong; Kantoush, Sameh A.; Phan, Cao Duong; Nguyen, Thi Phuong Mai; Binh, Doan Van (Hue University Journal of Science: Natural Science, 2025)
Salinity intrusion poses a threat to water security, agriculture, and livelihoods in the Vietnamese Mekong Delta (VMD), particularly under the combined pressures of climate change and upstream hydrological developments. Accurate short- to mid-term salinity forecasts are essential for proactive water resource management. This study evaluates the performance of two machine learning models—Random Forest Regression (RFR) and Support Vector Regression (SVR)—for salinity forecasting using long-term observational data (1996–2023) from 44 monitoring stations in the VMD. To capture temporal dynamics, multitemporal lag features (1, 10, 20, 30, and 60 days) were generated from observed salinity records. Bayesian optimization and time-series cross-validation were used for model tuning. Results show that SVR performs best for short-term forecasts (1–3 days), achieving R2 and NSE up to 0.927–0.928, MAE ≈ 0.824 g/L, and RMSE ≈ 1.858 g/L, while RFR provides more stable predictions over longer horizons (4–7 days), maintaining R2/NSE values of 0.627 to 0.766 with lower errors. Additionally, the 20-day lag windows yielded the most accurate results, likely reflecting the influence of tidal cycles. These findings highlight the importance of selecting appropriate models and temporal features for various forecast horizons, providing a data-driven framework to enhance early warning systems and support adaptive water resource management in the VMD.