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http://dx.doi.org/10.3741/JKWRA.2019.52.12.963

Applicability study on urban flooding risk criteria estimation algorithm using cross-validation and SVM  

Lee, Hanseung (Disaster Prevention Research Division, National Disaster Management Research Institute)
Cho, Jaewoong (Disaster Prevention Research Division, National Disaster Management Research Institute)
Kang, Hoseon (Disaster Prevention Research Division, National Disaster Management Research Institute)
Hwang, Jeonggeun (Disaster Prevention Research Division, National Disaster Management Research Institute)
Publication Information
Journal of Korea Water Resources Association / v.52, no.12, 2019 , pp. 963-973 More about this Journal
Abstract
This study reviews a urban flooding risk criteria estimation model to predict risk criteria in areas where flood risk criteria are not precalculated by using watershed characteristic data and limit rainfall based on damage history. The risk criteria estimation model was designed using Support Vector Machine, one of the machine learning algorithms. The learning data consisted of regional limit rainfall and watershed characteristic. The learning data were applied to the SVM algorithm after normalization. We calculated the mean absolute error and standard deviation using Leave-One-Out and K-fold cross-validation algorithms and evaluated the performance of the model. In Leave-One-Out, models with small standard deviation were selected as the optimal model, and models with less folds were selected in the K-fold. The average accuracy of the selected models by rainfall duration is over 80%, suggesting that SVM can be used to estimate flooding risk criteria.
Keywords
Risk criteria; Watershed characteristic; Limit rainfall; Support Vector Machine; Cross-validation;
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