한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권3호
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  • Pages.341-346
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  • 2022
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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홍수 위험도 판별을 위한 CNN 기반의 분류 모델 구현

Implementation of CNN-based classification model for flood risk determination

  • Cho, Minwoo (Department of Computer Engineering, Paichai University) ;
  • Kim, Dongsoo (Department of Computer Engineering, Paichai University) ;
  • Jung, Hoekyung (Department of Computer Engineering, Paichai University)
  • 투고 : 2021.11.23
  • 심사 : 2021.11.29
  • 발행 : 2022.03.31
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초록

지구온난화 및 이상 기후로 인해 홍수의 빈도 및 피해 규모가 늘어나고 있으며, 홍수 취약 지역에 노출된 사람이 2000년도에 비하여 25% 증가하였다. 홍수는 막대한 금전적, 인명적 손실을 유발하며, 홍수로 인한 손실을 줄이기 위해 홍수를 미리 예측하고 빠른 대피를 결정해야 한다. 본 논문은 홍수 예측을 위한 핵심 데이터인 강우량과 수위 데이터를 활용하여 시기적절한 대피 결정이 이루어질 수 있도록 CNN기반 분류 모델을 활용하여 홍수 위험도 판별 모델을 제안한다. 본 논문에서 제안한 CNN 기반 분류 모델과 DNN 기반의 분류 모델의 결과를 비교하여 더 좋은 성능을 보이는 것을 확인하였다. 이를 통해 홍수의 위험도를 판별하여, 대피 여부 판단하며 최적의 시기에 대피 결정을 내릴 수 있도록 하는 초기 연구로서 활용할 수 있을 것으로 사료된다.

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

키워드

과제정보

This study was carried out with the support of R&D Program for Forest Science Technology (Project No. 2021340A00-2123-CD01) provided by Korea Forest Service(Korea Forestry Promotion Institute).

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