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Research Article

Possibility analysisof future droughts using long short term memory and standardized groundwater level index

LSTM과 SGI를 이용한 미래 가뭄 발생 가능성 분석

Jae Deok Lima
,
Jeong-Seok Yangb*
임 재덕a
,
양 정석b*
aMaster’s course, School of Civil and Environmental Engineering, Kookmin University, College of Creative Engineering
bProfessor, School of Civil and Environmental Engineering, Kookmin University, College of Creative Engineering
a국민대학교 창의공과대학 건설시스템공학부 석사과정
b국민대학교 창의공과대학 건설시스템공학부 교수
* Corresponding Author
29 February 2020. pp. 131-140
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Abstract

The purpose of this study is to analyze the possibility of future droughts by calculating the Standardized Groundwater level Index(SGI) after predicting groundwater level using Long Short Term Memory (LSTM) model. The groundwater level of the Kumho River basin was predicted for the next three years by using the LSTM model, and it was validated through RMSE after learning with observation data except the last three years. The temporal SGI was calculated by using the prediction data and the observation data. The calculated SGI was interpolated within the study area, and the spatial SGI was calculated as the average value for each catchment using the interpolated SGI. The possibility of spatio-temporal drought was analyzed using calculated spatio-temporal SGI. It is confirmed that there is a spatio-temporal difference in the possibility of drought. Through the improvement of deep learning model and diversification of validation method, it is expected to obtain more reliable prediction results and the expansion of study area can be used to respond to drought nationwide, and furthermore it can provide important information for future water resource management.

Keywords
Deep learning
Long short term memory (LSTM)
Standardized groundwater level index (SGI)
Drought
Kumho river

본 연구는 심층학습 기법인 Long Short Term Memory (LSTM)를 이용하여 지하수위를 예측 후 표준지하수위지수(Standardized Groundwater level Index, SGI)를 산정함으로써 미래 가뭄 발생 가능성의 분석을 목적으로 하고 있다. LSTM 모형을 이용하여 금호강 유역의 지하수위를 미래 3년에 대해 예측을 하였으며, 예측시 최근 3년을 제외한 관측 자료로 학습 후 RMSE를 통해 검증하였다. 예측 자료와 관측 자료를 이용하여 시간적 SGI를 산정하였다. 산정된 SGI는 연구 지역 내 보간을 하였고, 보간된 SGI는 소유역별 평균값으로 공간적 SGI를 산정하였다. 산정된 시공간적 SGI를 이용하여 시공간적 가뭄 발생 가능성에 대해 분석하였다. 시공간별로 가뭄 발생 가능성에서 차이가 발생하는 것을 확인하였다. 향후 심층학습 모형의 개선 및 검증 방법의 다양화를 통해 신뢰성이 더욱 높은 예측 결과를 도출할 수 있고, 연구 적용 지역의 확대를 통해 전국적인 가뭄 대응 정책에 활용이 될 수 있으며, 더 나아가 미래 수자원 관리 차원에서 중요한 정보를 제공할 수 있을 것이다.

키워드
심층학습
LSTM
표준지하수위지수 (SGI)
가뭄
금호강
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 53
  • No :2
  • Pages :131-140
  • Received Date : 2020-01-13
  • Revised Date : 2020-02-01
  • Accepted Date : 2020-02-01
  • DOI :https://doi.org/10.3741/JKWRA.2020.53.2.131
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