KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Analysis of Urban Infrastructure Risk Areas to Flooding using Neural Network in Seoul

인공신경망을 활용한 서울시 도시기반시설 침수위험지역 분석

  • Received : 2015.02.11
  • Accepted : 2015.06.20
  • Published : 2015.08.01
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Abstract

This study analyzed urban infrastructure risk to flooding based on the possibility map of flooding calculated by neural network model focusing on Seoul. This study found that Gangnam-gu, Songpa-gu, Seocho-gu and Seodaemun-gu contained relatively large high-risk areas to flooding. Over $4.17km^2$ of transportation facilities were located in high-risk area to flooding and Gangnam-gu included over $0.85km^2$ of infrastructures exposed to high inundation risk. This study is meaningful in that it first applied the neural network modeling to flooding risk assesment and results of risk assessment can be incorporated into various planning process.

본 연구는 서울시를 대상으로 인공신경망을 활용하여 침수발생가능성과 침수위험지역을 도출하고, 위험지역 내 도시기반시설 현황을 살펴보았다. 분석결과, 강남구, 송파구, 서초구, 서대문구 등에서 침수발생가능성이 높은 위험지역을 많이 포함하고 있었다. 교통시설의 $4.17km^2$이상이 위험지역에 분포하여 우선 관리시설로 나타났고, 강남구 지역은 침수위험이 높은 기반시설을 $0.85km^2$이상 포함하고 있었다. 본 연구는 인공신경망 모델을 침수발생가능성 분석에 활용하여 그 적용가능성을 확인하였으며, 평가결과는 다양한 계획과정에 반영될 수 있을 것이다.

Keywords

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  1. Urban Flood Risk Assessment Considering Climate Change Using Bayesian Probability Statistics and GIS : A Case Study from Seocho-Gu, Seoul vol.19, pp.4, 2016, https://doi.org/10.11108/kagis.2016.19.4.036