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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-Time Flood Disaster Prediction System by Applying Machine Learning Technique


    Additional title:

    KSCE J Civ Eng


    Contributors:
    Keum, Ho Jun (author) / Han, Kun Yeun (author) / Kim, Hyun Il (author)

    Published in:

    Publication date :

    2020-09-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    FLOOD DISASTER EVACUATION LIFEBOAT

    BABA KEIJI | European Patent Office | 2017

    Free access

    SHELTER FOR FLOOD DISASTER

    NAKAMURA MASA | European Patent Office | 2017

    Free access

    APPLYING MACHINE LEARNING TO TAXI-TIME PREDICTION AT TOKYO INTERNATIONAL AIRPORT

    Kato, F. / Itoh, E. | British Library Conference Proceedings | 2022


    Floatable house in flood disaster areas

    JI XIANG / HUANG FEIYU / LIU XIAOBIN et al. | European Patent Office | 2021

    Free access

    Real-time travel-time prediction method applying multiple traffic observations

    Lim, Sung Han / Kim, Youngho / Lee, Chungwon | Springer Verlag | 2016