Combined Hydraulic and Black-Box Models for Flood Forecasting in Urban Drainage Systems

Abstract

Rapid urbanization and its implications for both water quality issues and floods have increased the need for modeling of urban drainage systems. Many operational models are based on deterministic solutions of hydraulic equations. Improving such models by adding a “black-box” component to deal with any systematic structure in the residuals is proposed. In this study, a conventional deterministic stormwater drainage network model is first developed for a rapidly developing catchment using the HYDROWORKS (now called Infoworks) package, from Wallingford Software in the United Kingdom. However, despite the generally satisfactory results, the HYDROWORKS model tended to underestimate the flow volume. In this paper, a black-box or “systems” model is fitted to the hydraulic urban drainage model in order to improve its overall efficiency. A study was conducted of suitable black-box models, which included the nonlinear artificial neural network model (ANN), and the linear time series models of Box and Jenkins in 1976. They were added to either the output (in simulation mode) or, in updating mode, to the residuals (i.e., difference between modeled and measured output) of the deterministic hydraulic model. The updating procedure provided a considerable improvement in the overall model efficiency for different lead-time forecasting. In simulation mode, however, only the nonlinear ANN model gave better performance in calibration, and a slight improvement in validation.