Hybrid Approach for Modeling Wet Weather Response in Wastewater Systems

This paper presents a hybrid modeling approach for modeling flows within wastewater pipe networks. The approach utilizes the MOUSE model as a deterministic pipe network model and a radial basis function neural network (RBFNN) model as a stochastic error-correction model. Both models utilize rainfall as input, whereas the RBFNN utilizes MOUSE model flow predictions and its errors (residuals, differences) related to that prediction. The MOUSE model output provides an approximation of the hydrodynamic process, whereas the outputs of the trained RBFNN compensate for the output errors (residuals) of the MOUSE model. It is demonstrated that this approach is capable of reducing the model prediction error compared to the deterministic model when applied alone. It is also demonstrated that this approach generates more accurate results than hybrid models with linear stochastic components when compared for direct and iterative forecasts. The results achieved are promising, and the approach developed provides an innovative tool for achieving more accurate outputs.