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Water 2019, 11(2), 293; https://doi.org/10.3390/w11020293

Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods

1
Department of Civil Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu Daegu 41566, Korea
2
Disaster Prevention Research Institute, Kyungpook National University, 80 Daehak-ro, Buk-gu Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Received: 24 December 2018 / Revised: 24 January 2019 / Accepted: 31 January 2019 / Published: 8 February 2019
(This article belongs to the Special Issue Urban Rainfall Analysis and Flood Management)
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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. View Full-Text
Keywords: real-time flood prediction; drainage system; urban inundation model; artificial neural network (ANN) real-time flood prediction; drainage system; urban inundation model; artificial neural network (ANN)
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Kim, H.I.; Keum, H.J.; Han, K.Y. Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods. Water 2019, 11, 293.

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