LAUSR

As rainfall intensity varies irregularly, urban floods can cause extreme damage. Furthermore, they are extremely nonlinear phenomena that are complex to analyze. Therefore, a classification-based real-time flood prediction model for urban areas is constructed in this study, by combining a numerical analysis model based on hydraulic theory with a machine learning model. Flood databases are constructed in advance for different rainfall scenarios using the Environmental Protection Agency-Storm Water Management Model (EPA-SWMM) and a two-dimensional inundation model. The flood depth data for each map grid are divided into five categories based on the average flood depth using the Latin hypercube sampling (LHS) and probabilistic neural network (PNN) classification techniques for higher-precision flood range prediction. A model is constructed to predict the representative cumulative volume if the observed rainfall is entered. For spatial expansion of the flood depth with the predicted representative cumulative volume, a system capable of generating a real-time flood map is constructed by linking the cumulative volume of each grid with the representative cumulative volume using linear and nonlinear regression. When compared with the results of a verified two-dimensional (2D) flood model, the developed-model goodness-of-fit is 85%, with a required run time of 1 min 12 s. Using the developed system, rainfall-induced flooding can potentially be predicted, facilitating disaster risk management and minimizing damage to property and health.