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2022 Vol.32, Issue 4 Preview Page

Research Article

Development of Deep-Learning-Based Models for Predicting Groundwater Levels in the Middle-Jeju Watershed, Jeju Island

딥러닝 기법을 이용한 제주도 중제주수역 지하수위 예측 모델개발

Jaesung Park1
,
Jiho Jeong2
,
Jina Jeong3*
,
Ki-Hong Kim4
,
Jaehyeon Shin5
,
Dongyeop Lee5
,
Saebom Jeong5
박 재성1
,
정 지호2
,
정 진아3*
,
김 기홍4
,
신 재현5
,
이 동엽5
,
정 새봄5
1Master Student, Department of Geology, Kyungpook National University
2Ph.D. Student, Department of Geology, Kyungpook National University
3Assistant Professor, Department of Geology, Kyungpook National University
4Division Director, Digital Convergence Division, Jeju Special Self-Governing Province
5Assistant Director, Digital Convergence Division, Jeju Special Self-Governing Province
1경북대학교 지질학과 석사과정
2경북대학교 지질학과 박사과정
3경북대학교 지질학과 조교수
4제주특별자치도 디지털융합과 과장
5제주특별자치도 디지털융합과 주무관
*Corresponding Author
31 December 2022. pp. 697-723
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Abstract

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

Keywords
groundwater monitoring data
precipitation monitoring data
groundwater usage monitoring data
groundwater level prediction
deep learning model

본 연구에서는 제주도의 중제주 수역 내에 위치하는 총 12개 지하수 관정에서 미래 30일 기간의 지하수위를 예측할 수 있는 모델을 개발하였다. 예측 모델개발을 위해 시계열 예측에 적합한 딥러닝 기법의 하나인 누적 장단기 메모리(stacked-LSTM) 기법을 이용하였으며, 2001년에서 2022년 동안 관측된 일 단위 강수량, 지하수 이용량 및 지하수위 자료가 예측 모델개발에 활용되었다. 특히, 본 연구에서는 입력자료의 종류 및 과거 자료의 순차 길이에 따라 다양한 모델을 구축하고 성능을 비교함으로써 딥러닝 기반 예측 모델개발에서 고려하여야 할 사항에 대한 검토와 절차를 제시하였다. 예측 모델개발 결과, 강수량, 지하수 이용량 및 과거 지하수위를 모두 입력자료로 활용하는 모델의 예측성능이 가장 뛰어난 것으로 확인되었으며, 입력으로 활용되는 과거 자료의 순차가 길수록 예측의 성능이 향상됨을 확인하였다. 이는 제주도의 깊은 지하수위 심도로 인하여 강수와 지하수 함양 간 지연시간이 길기 때문으로 판단된다. 이뿐만 아니라, 지하수 이용량 자료의 경우, 모든 이용량 자료를 활용하는 것보다 예측하고자 하는 지점의 지하수위에 민감한 영향을 주는 관정을 선별하여 입력자료로 이용하는 것이 예측 모델의 성능 개선에 긍정적 영향을 주는 것을 확인하였다. 본 연구에서 개발된 지하수위 예측 모델은 현재의 강수량 및 지하수 이용량을 기반으로 미래의 지하수위를 예측할 수 있어 미래의 지하수량에 대한 건전성 정보를 제공함에 따라 적정 지하수량 유지를 위한 다양한 관리방안 마련에 도움이 될 것으로 판단된다.

키워드
지하수위 자료
강수량 자료
지하수 이용량 자료
지하수위 예측
딥러닝 모델
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Information
  • Publisher :Korean Society of Engineering Geology
  • Publisher(Ko) :대한지질공학회
  • Journal Title :The Journal of Engineering Geology
  • Journal Title(Ko) :지질공학
  • Volume : 32
  • No :4
  • Pages :697-723
  • Received Date : 2022-12-15
  • Revised Date : 2022-12-28
  • Accepted Date : 2022-12-28
  • DOI :https://doi.org/10.9720/kseg.2022.4.697
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