대한토목학회논문집 (KSCE Journal of Civil and Environmental Engineering Research)

  • 제37권6호
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  • Pages.981-987
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  • 2017
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  • 1015-6348(pISSN)
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  • 2799-9629(eISSN)
대한토목학회 (Korean Society of Civil Engeneers)
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인공신경망 모형을 이용한 제주 지하수위의 장기예측

Long-term Prediction of Groundwater Level in Jeju Island Using Artificial Neural Network Model

  • 정일문 (한국건설기술연구원 수자원.하천연구소) ;
  • 이정우 (한국건설기술연구원 수자원.하천연구소) ;
  • 장선우 (한국건설기술연구원 수자원.하천연구소)
  • Chung, Il-Moon (Korea Institute of Civil engineering and building Technology) ;
  • Lee, Jeongwoo (Korea Institute of Civil engineering and building Technology) ;
  • Chang, Sun Woo (Korea Institute of Civil engineering and building Technology)
  • 투고 : 2017.10.10
  • 심사 : 2017.11.03
  • 발행 : 2017.12.01
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초록

투수성이 큰 화산섬인 제주도에서는 땅속으로 함양된 지하수자원이 가장 중요한 수원이므로 지하수의 적정관리가 매우 중요하다. 특히 가뭄시 지하수의 이용은 염수침투를 유발할 수 있으므로 지하수위 강하에 따른 단계별 제한 조치가 마련되어 있다. 농업용 지하수위에 대한 적정 지하수 이용을 위해서는 보다 장기적인 예측을 통해 사전에 대비하는 것이 필요하다. 이에 본 연구에서는 인공신경망 모형을 이용한 지하수위의 월단위예측기법을 개발하였고, 대표적인 관측공에 대해 적용하였다. 월단위 지하수위를 예측한 결과 학습 및 검증기간 모두 예측 성능이 우수한 것으로 분석되었다. 또한 장기예측을 위해서 입력인자로 월단위 지하수위 예측치를 순차적으로 이용하는 연속지하수위예측 모형을 구축하고 수개월 동안 무강수의 극한조건에 대한 지하수위 저하 양상을 분석하였다.

Jeju Island is a volcanic island which has a large permeability. Groundwater is a major water resources and its proper management is essential. Especially, there is a multilevel restriction due to the groundwater level decline during a drought period to protect sea water intrusion. Preliminary countermeasure using long-term groundwater level prediction is necessary to use agricultural groundwater properly. For this purpose, the monthly groundwater level prediction technique by Artificial Neural Network model was developed and applied to the representative monitoring wells. The monthly prediction model showed excellent results for training and test periods. The continuous groundwater level prediction model also developed, which used the monthly forecasted values adaptively as input data. The characteristics of groundwater declines were analyzed under extreme cases without precipitation for several months.

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참고문헌

  1. Coppola, E. A. Jr., Rana, A. J., Poulton, M. M., Szidarovszky, F. and Uhl, V. W. (2005). "A neural network model for predicting aquifer water level elevations." Ground Water, 2005, Vol. 43, No. 2, pp. 231-241. https://doi.org/10.1111/j.1745-6584.2005.0003.x
  2. Jeju Special Self-Governing Province (2013). Comprehensive Water Resouces Plan for Jeju-Island, p. 366 (in Korean).
  3. Jeju Special Self-Governing Province (2016). Determination of expanded management water levels and development of groundwater prediction model, p. 112 (in Korean).
  4. Kim, J. W., Koh, G. W., Won, J. H. and Han, C. (2005). "A study on the determination of management groundwater level on jeju island." Journal of KoSSGE, Vol. 10, No. 2, pp. 12-19 (in Korean).
  5. McCulloch, W. S. and Pitts, W. (1943). "A logical calculus of the ideas immanent in nervous activity." The Bulletin of Mathematical Biophysics, Vol. 5, No. 4, pp. 115-133. https://doi.org/10.1007/BF02478259
  6. Mohanty, S., Jha, M. K., Kumar, A. and Sudheer, K. P. (2010). "Artificial neural network modeling for groundwater level forecasting in a river island of eastern india." Water Resources Management, Vol. 24, No. 9, pp. 1845-1865. https://doi.org/10.1007/s11269-009-9527-x
  7. Sung, J. Y., Lee, J., Chung, I. M. and Heo, J. H. (2017). "Hourly water level forecasting at tributary affected by main river condition." Water, Vol. 9, No. 9, 644, doi:10.3390/w9090644.
  8. Yi, M. J. and Lee, K. K. (2004). "Transfer function-noise modelling of irregularly observed grounfwater heads using precipitation data." Journal of Hydrology, Vol. 285, No. 3, pp. 272-287. https://doi.org/10.1016/j.jhydrol.2003.09.005
  9. Yoon, H., Kim, Y., Ha, K. and Kim, G. B. (2013). "Application of groundwater-level prediction models using data-based learning algorithms to national groundwater monitoring network data." The Journal of Engineering Geology, Vol. 23, No. 2, pp. 137-147 (in Korean). https://doi.org/10.9720/kseg.2013.2.137