Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods

Ho Keum,Hyun Kim,Kun Han

doi:10.3390/w11020293

Abstract

Damage caused by flash floods is increasing due to urbanization and climate change, thus it is important to recognize floods in advance. The current physical hydraulic runoff model has been used to predict inundation in urban areas. Even though the physical calculation process is astute and elaborate, it has several shortcomings in regard to real-time flood prediction. The physical model requires various data, such as rainfall, hydrological parameters, and one-/two-dimensional (1D/2D) urban flood simulations. In addition, it is difficult to secure lead time because of the considerable simulation time required. This study presents an immediate solution to these problems by combining hydraulic and probabilistic methods. The accumulative overflows from manholes and an inundation map were predicted within the study area. That is, the method for predicting manhole overflows and an inundation map from rainfall in an urban area is proposed based on results from hydraulic simulations and uncertainty analysis. The Second Verification Algorithm of Nonlinear Auto-Regressive with eXogenous inputs (SVNARX) model is used to learn the relationship between rainfall and overflow, which is calculated from the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). In addition, a Self-Organizing Feature Map (SOFM) is used to suggest the proper inundation area by clustering inundation maps from a 2D flood simulation model based on manhole overflow from SWMM. The results from two artificial neural networks (SVNARX and SOFM) were estimated in parallel and interpolated to provide prediction in a short period of time. Real-time flood prediction with the hydraulic and probabilistic models suggested in this study improves the accuracy of the predicted flood inundation map and secures lead time. Through the presented method, the goodness of fit of the inundation area reached 80.4% compared with the verified 2D inundation model.

Highlights

Flooding in urban areas has caused human casualties and property damage, and social, environmental, economic, and psychological damage
This study aimed to develop an effective real-time inundation prediction technique using minimum data to ensure a sufficient lead time and prepare for urban flooding
SVNARX was constructed by applying rainfall and manhole overflow data, and scenario and actual torrential rainfall data were compared

Summary

Introduction

Flooding in urban areas has caused human casualties and property damage, and social, environmental, economic, and psychological damage. It causes damage to urban infrastructure, such as roads and subways, as well as critical utilities including electricity, gas, and water supply. This is followed by paralysis of urban functions and collapse of the social system. Inundation in urban areas occurs due to a lack of flow conveyance by pipes during heavy rainfall [1]. Flood damage in metropolitan cities arises due to insufficient capacity of sewer pipes and drainage facilities, rather than by river inundation [2].

Objectives

Methods

Conclusion