Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Impact of Baekrokdam precipitation observation data on improving groundwater level prediction in mid-mountainous region of Jeju Island

백록담 강수량 관측자료가 제주도 중산간지역 지하수위 예측 향상에 미치는 영향

  • Shin, Mun-Ju (Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation) ;
  • Kim, Jeong-Hun (Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation) ;
  • Kang, Su-Yeon (Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation) ;
  • Moon, Soo-Hyoung (Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation) ;
  • Hyun, Eun Hee (R&D Innovation Division, Jeju Special Self-Governing Province Development Corporation)
  • 신문주 (제주특별자치도개발공사 먹는물연구소) ;
  • 김정훈 (제주특별자치도개발공사 먹는물연구소) ;
  • 강수연 (제주특별자치도개발공사 먹는물연구소) ;
  • 문수형 (제주특별자치도개발공사 먹는물연구소) ;
  • 현은희 (제주특별자치도개발공사 R&D혁신본부)
  • Received : 2024.08.14
  • Accepted : 2024.09.23
  • Published : 2024.10.31
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Abstract

Groundwater is an important water resource used for various purposes along with surface water. Jeju Island relies on groundwater for most of its water use, so predicting and managing groundwater volume is very important for sustainable use of groundwater. In this study, precipitation data from the Baekrokdam Climate Change Observatory was additionally used to accurately predict groundwater levels. We compared and analyzed the improvement in monthly groundwater level prediction performance of the ANN and LSTM models for two observation wells located in the mid-mountainous area of the Pyoseon watershed in Jeju Island. As a result, when Baekrokdam precipitation data was not used, the NSE values of the two artificial intelligence models were over 0.871, showing very high groundwater level prediction performance. The LSTM model showed relatively higher prediction performance at high and low groundwater levels than the ANN model. We found that the prediction performance decreases as the variation characteristics of the groundwater level become more complex. When Baekrokdam precipitation data was additionally used, the NSE values of the two artificial intelligence models were above 0.907, indicating improved prediction performance, and the NSE value was improved by up to 0.036. This means that when additional rainfall in the upstream area is used, the artificial intelligence model can more appropriately interpret the fluctuating characteristics of the groundwater level. In addition, the additional use of Baekrokdam precipitation data further helped improve groundwater level prediction for observation well, where groundwater level prediction is relatively difficult, and artificial intelligence models, which have relatively low groundwater level prediction performance. In particular, when Baekrokdam precipitation data was additionally used for a specific observation well, the groundwater level prediction performance of the ANN model was improved to a level comparable to that of the LSTM model. The methods and results of this study can be useful in future research using artificial intelligence models.

지하수는 지표수와 함께 다양한 용수로 사용되는 중요한 수자원이며, 특히 제주도의 경우 대부분의 용수를 지하수에 의존하고 있기 때문에 지속가능한 지하수의 이용을 위해 지하수량의 예측 및 관리는 매우 중요하다. 본 연구에서는 정확한 지하수위 예측을 위해 백록담 기후변화관측소의 강수량 데이터를 추가적으로 사용하여 제주도 표선유역 중산간 지역에 위치한 2개 관측정에 대해 ANN 모델과 LSTM 모델의 월 단위 지하수위 예측성능 개선효과를 비교분석 하였다. 그 결과, 백록담 강수량 데이터를 사용하지 않은 경우 두 인공지능 모델의 NSE 값은 0.871 이상을 보여 높은 지하수위 예측성능을 보였다. LSTM 모델은 ANN 모델보다 고수위 및 저수위에서 상대적으로 높은 예측성능을 나타내었으며, 관측지하수위의 변동폭이 커 변동특성이 복잡할수록 예측성능이 낮아짐을 확인하였다. 백록담 강수량 데이터를 추가적으로 사용한 경우 두 인공지능 모델의 NSE 값은 0.907 이상을 보여 개선된 예측성능을 나타내었으며, NSE 값은 최대 0.036만큼 개선되었다. 이것은 상류지역의 강수량을 추가적으로 사용 시 지하수위 해석에 도움이 되는 정보가 많아져 인공지능 모델은 지하수위의 변동특성을 더욱 적절히 해석할 수 있다는 것을 의미한다. 또한 관측지하수위 변동폭이 더 커 지하수위 예측이 상대적으로 어려운 관측정일수록 그리고 지하수위 예측성능이 상대적으로 낮은 인공지능 모델일수록 백록담 강수량 데이터의 추가사용시 지하수위 예측 개선에 더욱 도움이 되었다. 특히, 특정 관측정에 대해 백록담 강수량 데이터를 추가적으로 사용 시 ANN 모델의 지하수위 예측성능은 LSTM 모델과 대등한 수준으로 개선되었다. 본 연구의 방법 및 결과는 향후 인공지능 모델을 활용한 연구에서 유용하게 사용될 수 있다.

Keywords

Acknowledgement

본 연구는 제주지방기상청에서 백록담 기후변화관측소의 기상자료를 제공받아 연구에 사용하였습니다. 자료 제공에 감사드립니다.

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