A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network

Abstract

Dynamic flow forecast, which is one of the critical technologies in the field of future Intelligent Drainage, has great potential for mitigating the damages resulting from extreme rainfalls. This study aims to develop a coupled neural network called RBF-NARX Forecast Model (RNFM) to predict urban drainage outflow. RNFM integrates the architecture advantages of the radial basis function neural network (RBFNN) and the nonlinear autoregressive with an exogenous inputs neural network (NARXNN). By calculating the Square Sum of Error (SSE) between RNFM predictions and SWMM simulations, the network parameters are optimized and the optimal coupling site of RBFNN and NARXNN is found. The urban drainage in Tianjin is presented to justify the feasibility of RNFM, and the average SSE in test rainfalls is only 0.273. Based on the Monte Carlo simulations (MCS), the uncertainty analysis is quantified and the SWMM simulations lie within the 95% prediction confidential interval, which proves that RNFM have great potential in predictions and management of urban runoff.