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2020 Vol.7, Issue 4 Preview Page

Original Article

Forecasting Technique of Downstream Water Level using the Observed Water Level of Upper Stream

수계 상류 관측 수위자료를 이용한 하류 홍수위 예측기법

Sang Mun Kim1
,
Byungwoong Choi2*
,
Namjoo Lee3
김 상문1
,
최 병웅2*
,
이 남주3
1Deputy General Manager, Safety & Disaster Management, Seoul 08502, Korea
2Researcher, Research Team on Ecological and Natural Map, National Institute of Ecology, Seocheon 33657, Korea
3Professor, Department of Civil Engineering, Kyungsung University, Busan 48434, Korea
1SDMENC 차장
2국립생태원 생태자연도연구팀 연구원
3경성대학교 토목공학과 교수
*Corresponding Author
31 December 2020. pp. 345-352
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Abstract

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

Keywords
Artificial Neural Network
Flood Warning
Lead Time
Multiple Regression Analysis
Water Level Forecast

최근 하천범람에 따른 피해를 최소화하기 위해서는 대피를 위한 선행시간을 확보하는 것이 매우 중요하다. 본 연구에서는 현재 하천에서 측정되고 있는 수위 관측 자료를 이용하여 이상호우 발생시 하류의 수위를 예측하였다. 수위 예측을 위해 다중회귀모형 및 인공신경망 모형을 섬강시험유역에 적용하였다. 다중회귀모형 및 인공신경망 모형의 학습에는 섬강시험유역의 2002년부터 2010년까지의 수위 관측 자료를 이용하였으며, 학습된 모형을 이용하여 발생 가능한 수위를 예측하였다. 모의 결과 인공신경망 수위예측모형의 결정계수는 0.991 - 0.999로 나타났으며, 다중회귀수위예측 모형의 결정계수는 0.945 - 0.990로 나타나 인공신경망을 이용한 수위예측모형이 다중회귀모형보다 좀 더 나은 예측 결과를 나타내는 것을 확인할 수 있었다. 본 연구결과는 향후 하천에서 선행시간을 확보한 홍수 예보 구축에 활용할 수 있을 것으로 판단된다.

키워드
인공신경망
홍수 예보
선행시간
다중회귀분석
수위 예측
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Information
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 7
  • No :4
  • Pages :345-352
  • Received Date : 2020-10-18
  • Revised Date : 2020-11-09
  • Accepted Date : 2020-11-10
  • DOI :https://doi.org/10.17820/eri.2020.7.4.345
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