Application of Machine Learning in Water Quality Prediction and Analysis for River Cross-Sections

• • STD-Bayesian-XGBoost outperformed STD-Bayesian-RF, achieving RMSE reductions of 15-20% and R² > 0.90 for DO, NH4+-N, TP, and CODMn predictions, enabling more reliable early warning systems. • • Seasonal trend decomposition (STD) effectively denoised and extracted seasonal factors from water quality time series, improving model stability and predictive accuracy under fluctuating environmental conditions. • • Bayesian optimization efficiently tuned hyperparameters, enhancing model flexibility and precision without manual intervention, reducing computational cost by approximately 30% compared to grid search. • • The methodology is transferable across different climatic and hydrological regions, offering a scalable solution for water quality forecasting in data-scarce areas.