Application of Particle Swarm Optimization for Auto-Tuning of the Urban Flood Model

Reliable time-efficient prediction of urban floods is one of the essential tasks for planning of disaster prevention and mitigation measures. A key challenge of urban flood models is to obtain reliable input data. While geometric data can be directly measured, some other data, such as roughness and head loss of each flow system, are not easy to measure. This study proposes a novel approach for the auto-tuning of these unmeasurable data based on Particle Swarm Optimization (PSO). In this paper, we first performed a sensitivity analysis of the present urban flood model to find important parameters, which dominantly determine the predictive skills of the present urban flood model. We then developed a PSO-based auto-tuning system for estimation of these parameters. The entire computation domain was evenly split into square segments, and optimum values of these parameters were determined in each segment. The capability of this method was confirmed by comparisons of Nash–Sutcliffe efficiency, normalized root-mean square error, Kling–Gupta efficiency, and Akaike Information Criteria. As a result, it was found that important parameters for the present urban flood model were Manning’s roughness of the pipeline and a coefficient for determination of the discharge from the ground surface to sewer pipelines. It was also found that the present PSO-based auto-tuning system showed reasonably good performance in tuning these parameters, which clearly improve the predictive skills of the present urban flood model.