Hybrid framework for rapid prediction, sparse flow field reconstruction, and boundary inversion of pollutant...

Urban lakes face increasing pollution pressures, yet accurate prediction of contaminant transport and source identification remains challenging due to the high computational cost and sparse monitoring data. This study develops a hybrid framework based on proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for three tasks: rapid prediction of pollutant fields under new boundary conditions, full flow field reconstruction from sparse observations, and inversion of key boundary condition parameters. The POD-RBF fast prediction model achieves excellent accuracy (coefficient of determination R² > 0.97, mean absolute error MAE = 0.35) while reducing computation time minutes to seconds. With only 10 monitoring points, flow fields reconstruction from sparse data yields MAE = 0.042 and mean squared error MSE = 0.004. Using 20 monitoring points and the Nelder-Mead optimizer, boundary inversion recovers COD concentration with a relative error of 1.6% and achieves an excellent fit at monitoring points (R² = 0.98). SHAP (SHapley Additive exPlanations) analysis identifies discharge concentration as the most influential factor on pollutant dispersion, followed by diffusion time and rainfall intensity. The proposed framework provides an accurate, computationally efficient, and data-sparse solution for real-time water quality forecasting and source identification in urban lakes, particularly valuable for emergency response in data-scarce environments.