Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Development of a method for urban flooding detection using unstructured data and deep learing

비정형 데이터와 딥러닝을 활용한 내수침수 탐지기술 개발

  • Lee, Haneul (Program in Smart City Engineering, Inha University) ;
  • Kim, Hung Soo (Department of Civil Engineering, Inha University) ;
  • Kim, Soojun (Department of Civil Engineering, Inha University) ;
  • Kim, Donghyun (Program in Smart City Engineering, Inha University) ;
  • Kim, Jongsung (Program in Smart City Engineering, Inha University)
  • 이하늘 (인하대학교 스마트시티공학과) ;
  • 김형수 (인하대학교 사회인프라공학과) ;
  • 김수전 (인하대학교 사회인프라공학과) ;
  • 김동현 (인하대학교 스마트시티공학과) ;
  • 김종성 (인하대학교 스마트시티공학과)
  • Received : 2021.09.23
  • Accepted : 2021.10.27
  • Published : 2021.12.31
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Abstract

In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

본 연구에서는 비정형 데이터인 사진자료를 이용하여 침수의 발생여부를 판단하는 모델을 개발하였다. 침수분류를 모델 개발을 위하여 CNN기반의 VGG16, VGG19을 이용하였다. 모델을 개발하기 위하여 침수사진과 침수가 발생하지 않은 사진을 웹크롤링 방법을 이용하여 사진을 수집하였다. 웹크롤링 방법을 이용하여 수집한 데이터는 노이즈 데이터가 포함되어 있기 때문에 1차적으로 본 연구와 상관없는 데이터는 소거하였으며, 2차적으로 모델 적용을 위하여 224 × 224로 사진 사이즈를 일괄 변경하였다. 또한 사진의 다양성을 위해서 사진의 각도를 변환하여 이미지 증식을 수행하였으며. 최종적으로 침수사진 2,500장과 침수가 발생하지 않은 사진 2,500장을 이용하여 학습을 수행하였다. 모델 평가결과 모델의 평균 분류성능은 97%로 나타났으며. 향후 본 연구결과를 통하여 개발된 모델을 CCTV관제센터 시스템에 탑재한다면 신속하게 침수피해에 대한 대처가 이루어 질 수 있을 것이라 판단된다.

Keywords

Acknowledgement

이 논문은 행정안전부 재난피해 복구역량강화 기술개발사업의 지원을 받아 수행된 연구임(2021-MOIS36-002).

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