Estuarine salinity prediction using empirical mode decomposition and random forest for supporting water resource management.

Seawater intrusion during dry seasons presents significant challenges to water supply security and estuarine ecosystem management, highlighting the need for accurate salinity forecasting. However, the non-stationary nature of estuarine salinity time series complicates its prediction. This study employs Empirical Mode Decomposition (EMD) to decompose inputs (runoff, tide level, wind) and outputs (salinity) into intrinsic mode functions (IMFs) and integrates them with Random Forest (RF) for forecasting in the Pearl River Estuary. Seven hybrid models under the three EMD decomposition frameworks (X: decomposing inputs, Y: decomposing outputs, XY: decomposing both inputs and outputs) are proposed and compared against a traditional RF model. Results show that the EMD-RF hybrid model significantly improves the accuracy and stability of salinity forecasts. The XY-ANN framework, which combines decomposition of both inputs and outputs with Artificial Neural Networks (ANN) for integration, showed the highest predictive accuracy, achieving an NSE of 0.91. The proposed method could provide stable and accurate predictions over extended forecasting periods (1 ~ 30 days), without performance degradation. Upstream runoff and tidal factors were identified as key predictors for salinity forecasting. This study also highlights the significant role of low-frequency components in improving prediction accuracy, especially for long-term forecasts. Overall, the introduction of the decomposition framework enables a more efficient analysis of complex hydrological patterns, offering a simple and effective method for estuarine salinity prediction.