Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Analysis of Groundwater Level Prediction Performance with Influencing Factors by Artificial Neural Network

지하수위 영향인자에 따른 인공신경망 기반의 지하수위 예측 성능 분석

  • Kim, Incheol (Dept. Civil and Environmental Engrg., Univ. of Nebraska-Lincoln) ;
  • Lee, Jaehwan (Dept. Urban Infrastructure Research, Seoul Institute of Technology) ;
  • Kim, Junghwan (Dept. Smart City Research, Seoul Institute of Technology) ;
  • Lee, Hyoungkyu (Dept. Civil Engrg., Seoil Univ.) ;
  • Lee, Junhwan (Dept. Civil and Environmental Engrg., Yonsei Univ.)
  • Received : 2020.12.01
  • Accepted : 2021.04.08
  • Published : 2021.05.31
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Abstract

Groundwater level (GWL) causes the stress state within soil and affects the bearing capacity and the settlement of foundation. In this study, the analyses of influencing factors on GWL fluctuation were performed. From the results, river stage and moving average of precipitation were main influence components for urban near large river and rural areas, respectively. In addition, the prediction performance of GWL using artificial neural network (ANN) was conducted with respect to the influence components. As a result, the effect of main component was significant on the prediction performance of GWL.

지하수위 변동은 지반의 응력 상태에 변화를 일으켜 기초구조물의 지지력 및 침하에 직·간접적인 영향을 미칠 수 있다. 본 연구에서는 연구 대상지역을 선정하여 지하수위 영향인자 분석을 수행하였다. 그 결과 대상지역에 따라 지하수위에 미치는 영향인자들이 각각 달랐으며, 규모가 큰 하천변에 위치한 도심지역의 경우 하천수위가 지하수위 변동에 영향을 미치는 주요 인자였으며, 지표면 포장율이 낮은 도외지역의 경우는 선행강우를 고려하기 위해 도입된 강우이동평균이 주요 인자였다. 또한, 여러 입력 인자 조합을 고려하여 인공신경망을 통한 지하수위를 예측을 수행하였다. 분석결과 주요 지하수위 영향인자가 지하수위 예측 성능에 미치는 영향이 큰 것으로 나타났다. 결과적으로, 인공신경망을 이용하여 지하수위를 예측할때, 적절한 지하수위 영향인자 평가가 수행되어야 하며 이를 예측에 적용할 필요가 있는 것을 나타낸다.

Keywords

Acknowledgement

본 연구는 2019년 연세대학교대학원 연구장학금 및, 한국에너지기술평가원, 한국연구재단, 국토교통과학기술진흥원의 지원(Nos. 20194030202460, 2020R1A2C2011966, 20SMIP-A158708-01)으로 수행되었으며, 이에 깊은 감사를 드립니다.

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