한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제46권12호
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  • Pages.1249-1263
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  • 2013
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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다층 퍼셉트론 인공신경망 모형을 이용한 가뭄예측

Drought Forecasting Using the Multi Layer Perceptron (MLP) Artificial Neural Network Model

  • 이주헌 (중부대학교 공과대학 토목공학과) ;
  • 김종석 (서울시립대학교 공과대학 토목공학과) ;
  • 장호원 (중부대학교 공과대학 토목공학과) ;
  • 이장춘 (전북대학교 공과대학 자원에너지공학과)
  • Lee, Joo-Heon (Dept. of Civil Engineering, Joongbu University) ;
  • Kim, Jong-Suk (Dept. of Civil Engineering, University of Seoul) ;
  • Jang, Ho-Won (Dept. of Civil Eng., Joongbu University) ;
  • Lee, Jang-Choon (Dept. of Mineral Resources Energy Engineering Chonbuk National University)
  • 투고 : 2013.09.01
  • 심사 : 2013.12.02
  • 발행 : 2013.12.31
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초록

장기간의 가뭄에 의한 피해를 최소화하기 위해서는 유역에 적합한 가뭄관리 대책의 수립과 함께 미래에 발생하게 될 가뭄을 미리 예측할 수 있는 기술이 구축되어야 한다. 또한 미래의 가뭄에 대한 합리적 대응 방안을 수립하기 위해서는 가뭄의 지속기간(duration)과 심도(severity)의 정량적인 예측이 선행되어야 한다. 본 연구에서는 수문 시계열의 예측에 가장 많이 이용되고 있는 대표적인 통계학적 기법인 인공신경망 모형(Artificial Neural Network Model)과 가뭄지수를 이용하여 남한지역의 서울, 대전, 대구, 광주 등의 4개 기상관측소를 선정하여 가뭄예측을시도하였다. 가뭄 예측을 위하여 남한지역 내 선정한 기상관측소의 관측된 과거 강수량 자료를 이용하여 산정된 SPI (Standardized Precipitation Index)를 입력변수로 하여 다층 퍼셉트론(Multi Layer Perceptron) 인공신경망 모델에 적용하였으며, 매개변수 보정을 위한 학습기간으로 1976~2000년과 2001~2010년을 예측을 위한 검증기간으로 선정하여, 학습 및 예측을 시도하였다. 학습된 최적의 예측모형을 이용하여 서로 다른 선행예보시간(1~6개월)을 갖고 SPI (3), SPI (6), SPI (12)별로 가뭄을 예측하였으며, 가뭄예측 결과, SPI (3)의 경우에는 1개월 선행예보에서만 좋은 결과를 나타내었으며, SPI (6)의 경우 1~3개월 후의 가뭄을 예측하는 경우에 비교적 관측자료와 잘 일치하는 결과를 나타내었다. SPI (12)의 경우에는 약5개월 후까지의 가뭄예측에 양호한 결과를 나타내었다.

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

키워드

참고문헌

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  3. Sales Prediction of Electronic Appliances using a Convergence Model based on Artificial Neural Network and Genetic Algorithm vol.13, pp.9, 2015, https://doi.org/10.14400/JDC.2015.13.9.177
  4. Correlation analysis between Korean spring drought and large-scale teleconnection patterns for drought forecasting vol.21, pp.1, 2017, https://doi.org/10.1007/s12205-016-0580-8
  5. Quantitative characterization of historical drought events in Korea -focusing on outlier analysis of precipitation- vol.49, pp.2, 2016, https://doi.org/10.3741/JKWRA.2016.49.2.145
  6. Agricultural drought monitoring using the satellite-based vegetation index vol.49, pp.4, 2016, https://doi.org/10.3741/JKWRA.2016.49.4.305
  7. ROC evaluation for MLP ANN drought forecasting model vol.49, pp.10, 2016, https://doi.org/10.3741/JKWRA.2016.49.10.877
  8. Estimating Quantified Hydrological Input Value for Hydrological Drought vol.18, pp.5, 2018, https://doi.org/10.9798/KOSHAM.2018.18.5.11