Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods

Water quality monitoring is crucial for public health and environmental protection, but traditional methods lack real-time accuracy. This study addresses this gap by combining feature augmentation methods (e.g., sliding window) with Principal Component Analysis (PCA) and a tailored Two-Layer Regularized Gated Recurrent Unit (TLR-GRU) model for efficient water quality prediction. Results demonstrate that the proposed framework significantly improves prediction accuracy, with R2 increased by 7.78%, RMSE decreased by 27.36%, MAE by 36.71%, and MAPE by 45.08%. This approach offers a novel technical pathway for real-time environmental monitoring and water resource management.