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2019 Vol.52, Issue 4 Preview Page

Research Article

Meteorological drought outlook with satellite precipitation data using Bayesian networks and decision-making model

베이지안 네트워크 및 의사결정 모형을 이용한 위성 강수자료 기반 기상학적 가뭄 전망

Ji Yae Shina
,
Ji-Eun Kimb
,
Joo-Heon Leec
,
Tae-Woong Kimd*
신 지예a
,
김 지은b
,
이 주헌c
,
김 태웅d*
aPostdoctoral Researcher, Department of Civil and Environmental Engineering, Hanyang University
bPh.D. Student, Department of Civil and Environmental System Engineering, Hanyang University
cProfessor, Department of Civil Engineering, Joongbu University
dProfessor, Department of Civil and Environmental Engineering, Hanyang University
a한양대학교 공학대학 건설환경공학과 박사후연구원
b한양대학교 대학원 건설환경시스템공학과 박사과정
c중부대학교 건축토목공학부 교수
d한양대학교 공학대학 건설환경공학과 교수
* Corresponding Author
30 April 2019. pp. 279-289
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Abstract

Unlike other natural disasters, drought is a reoccurring and region-wide phenomenon after being triggered by a prolonged precipitation deficiency. Considering that remote sensing products provide consistent temporal and spatial measurements of precipitation, this study developed a remote sensing data-based drought outlook model. The meteorological drought was defined by the Standardized Precipitation Index (SPI) achieved from PERSIANN_CDR, TRMM 3B42 and GPM IMERG images. Bayesian networks were employed in this study to combine the historical drought information and dynamical prediction products in advance of drought outlook. Drought outlook was determined through a decision-making model considering the current drought condition and forecasted condition from the Bayesian networks. Drought outlook condition was classified by four states such as no drought, drought occurrence, drought persistence, and drought removal. The receiver operating characteristics (ROC) curve analysis were employed to measure the relative outlook performance with the dynamical prediction production, Multi-Model Ensemble (MME). The ROC analysis indicated that the proposed outlook model showed better performance than the MME, especially for drought occurrence and persistence of 2- and 3-month outlook.

Keywords
Bayesian networks
Decision-making model
Drought outlook
Satellite image data

가뭄재해는 다른 재해와 다르게 광범위한 공간에 걸쳐서 충분한 강우가 발생하기 전까지 오랜 기간 동안 발생되는 특성이 있다. 위성 영상은 시공간적으로 지속적인 강수량 관측을 제공할 수 있다. 본 연구는 위성 영상 기반의 강수자료를 활용하여 기상학적 가뭄 전망 모형을 개발하였다. PERSIANN_CDR, TRMM 3B42와 GPM IMERG 영상을 활용하여 강수 자료를 구축한 뒤, 표준강수지수(SPI)를 기반으로 기상학적 가뭄을 정의하였다. 과거의 가뭄 정보와 물리적 예측 모형 기반의 가뭄 예측 결과를 결합할 수 있는 베이지안 네트워크 기반 가뭄 예측 기법을 이용하여 확률론적 가뭄 예측 결과를 생산하였으며, 가뭄 예측결과를 가뭄 전망 의사결정 모형에 적용하여 가뭄 전망 결과를 도출하였다. 가뭄 전망 정보는 가뭄 발생, 지속, 종결, 가뭄 없음의 4단계로 구분하였다. 본 연구의 가뭄 전망 결과는 ROC 분석을 통하여 물리적 예측 모형인 다중모형 앙상블(MME)을 활용한 가뭄 전망 결과와 전망 성능을 비교하였다. 그 결과, 2∼3개월 가뭄 전망에 대한 가뭄 발생 및 지속의 단계에서는 MME 모형보다 높은 전망 성능을 보여주었다.

키워드
가뭄전망
위성 영상
베이지안 네트워크
의사결정모형
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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 : 52
  • No :4
  • Pages :279-289
  • Received Date : 2018-12-29
  • Revised Date : 2019-03-03
  • Accepted Date : 2019-03-15
  • DOI :https://doi.org/10.3741/JKWRA.2019.52.4.279
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