한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제55권2호
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  • Pages.121-133
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  • 2022
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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RCP 시나리오와 다층신경망 모형을 활용한 가뭄시 물부족량 예측

Predicting the amount of water shortage during dry seasons using deep neural network with data from RCP scenarios

  • 장옥재 (서울시립대학교 토목공학과) ;
  • 문영일 (서울시립대학교 토목공학과)
  • Jang, Ock Jae (Department of Civil Engineering, University of Seoul) ;
  • Moon, Young Il (Department of Civil Engineering, University of Seoul)
  • 투고 : 2021.11.09
  • 심사 : 2021.12.16
  • 발행 : 2022.02.28
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초록

기상학적으로 예년 대비 부족한 강우량으로 인해 발생하는 가뭄재해는 피해범위가 광범위하고, 그 발생과 소멸을 쉽게 파악하기 어려운 특징을 가지고 있다. 그렇기 때문에 가뭄 발생예상 지역과 그 물부족량을 신뢰성 있고, 신속하게 예측하는 것은 가뭄 대응체계 구축에서 중요한 요소이다. 하지만, 현재는 과거 약 50여년의 기상관측자료로 그 경우의 수가 제한된다는 문제점이 있다. 그렇기 때문에 과거에 발생한 가뭄과 양상이 다른 경우 가뭄으로 인한 물부족량을 예측하기 어려운 한계점이 있다. 이러한 문제 해결을 위해 본 연구에서는 4개 RCP 시나리오 자료를 물수지 분석 모형에 적용하여 360개년의 연간 물부족량을 산출하였다. 다음으로 다층신경망 모형의 입력값으로 RCP 시나리오로부터 산출된 SPEI 값을 그리고, 출력값으로 연간 물부족량을 적용하여 학습을 진행했다. 학습된 모형을 통해 과거 수 개월 동안의 SPEI값과 미래 예측되는 가뭄 상황을 입력함으로써 쉽고 신뢰성 있게 중권역별 예상 물부족량을 산정할 수 있다. 이는 의사결정자들이 가뭄 발생 이전에 효율적인 가뭄 대응책 수립에 도움이 될 것으로 판단된다.

The drought resulting from insufficient rainfall compared to the amount in an ordinary year can significantly impact a broad area at the same time. Another feature of this disaster is hard to recognize its onset and disappearance. Therefore, a reliable and fast way of predicting both the suffering area and the amount of water shortage from the upcoming drought is a key issue to develop a countermeasure of the disaster. However, the available drought scenarios are about 50 events that have been observed in the past. Due to the limited number of events, it is difficult to predict the water shortage in a case where the pattern of a natural disaster is different from the one in the past. To overcome the limitation, in this study, we applied the four RCP climate change scenarios to the water balance model and the annual amount of water shortage from 360 drought events was estimated. In the following chapter, the deep neural network model was trained with the SPEI values from the RCP scenarios and the amount of water shortage as the input and output, respectively. The trained model in each sub-basin enables us to easily and reliably predict the water shortage with the SPEI values in the past and the predicted meteorological conditions in the upcoming season. It can be helpful for decision-makers to respond to future droughts before their onset.

키워드

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