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

Hanseung Lee; Jaewoong Cho; Hoseon Kang; Jeonggeun Hwang

doi:10.3741/JKWRA.2019.52.12.963

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2019 Vol.52, Issue 12 Preview Page Next Page

Research Article

Applicability study on urban flooding risk criteria estimation algorithm using cross-validation and SVM 교차검증과 SVM을 이용한 도시침수 위험기준 추정 알고리즘 적용성 검토

31 December 2019. pp. 963-973

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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

본 연구는 도시침수 위험기준이 산정되지 않은 지역의 예경보 기준을 예측하기 위해 유역특성 자료와 피해이력 기반으로 산정된 한계강우량을 활용하여 도시침수 위험기준을 추정하는 모델을 검토하였다. 위험기준 추정모델은 머신러닝 알고리즘의 하나인 Support Vector Machine을 이용하여 설계하였으며, 학습자료는 지역별 한계강우량과 유역특성으로 구성하였다. 학습자료는 정규화 한 후 SVM 알고리즘에 적용하였으며, SVM에 적용시 Leave-One-Out과 K-fold 교차검증 알고리즘을 이용하여 절대평균오차와 표준편차를 계산한 후 모델의 성능을 평가하였다. Leave-One-Out의 경우 표준편차가 작은 모델이 최적모델로 선정되었으며, K-fold의 경우 fold의 개수가 적은 모델이 선정되었다. 선정된 모델의 지속시간별 평균 정확도는 80% 이상으로 나타나 침수 위험기준 추정을 위해 SVM을 활용가능 할 것으로 판단된다.

키워드

위험기준

유역특성

한계강우량

Support Vector Machine

교차검증

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Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 52
No :12
Pages :963-973
Received Date : 2019-07-30
Revised Date : 2019-11-14
Accepted Date : 2019-11-19
DOI :https://doi.org/10.3741/JKWRA.2019.52.12.963

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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