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2017 Vol.50, Issue 11 Preview Page
Bayesian networks-based probabilistic forecasting of hydrological drought considering drought propagation

가뭄의 전이 현상을 고려한 수문학적 가뭄에 대한 베이지안 네트워크 기반 확률 예측

Ji Yae Shina
,
Hyun-Han Kwonb
,
Joo-Heon Leec
,
Tae-Woong Kimd*
신 지예a
,
권 현한b
,
이 주헌c
,
김 태웅d*
aDepartment of Civil and Environmental Engineering, Hanyang University
bDepartment of Civil Engineering, Chonbuk National University
cDepartment of Civil Engineering, Joongbu University
dDepartment of Civil and Environmental Engineering, Hanyang University (ERICA)
a한양대학교 대학원 건설환경공학과
b전북대학교 토목공학과
c중부대학교 토목공학과,
d한양대학교 공학대학 건설환경공학과
*교신저자. *Corresponding Author. 
30 November 2017. pp. 769-779
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Abstract

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.

Keywords
Bayesian networks
Drought propagation
Hydrological drought
Probabilistic forecasting

최근 우리나라에서 빈번하게 발생되는 가뭄으로 인하여 많은 피해가 발생하고 있으며, 이에 대한 사전대응의 필요성이 커지고 있다. 가뭄에 대한 효과적인 사전대응을 위해서는 신뢰성 있는 가뭄 예측 정보가 필수적이다. 본 연구에서는 수문학적 가뭄에 대한 확률론적 예측을 수행하기 위하여 가뭄의 전이현상을 베이지안 네트워크 모형에 반영하였다. 가뭄의 전이현상을 고려한 베이지안 네트워크 기반의 가뭄 예측 모형(PBNDF)은 과거, 현재, 미래에 대한 다중 모형 앙상블 예측결과와 가뭄전이 관계를 결합하여 새로운 수문학적 가뭄 예측 결과를 생산하도록 구축되었다. 본 연구에서 PBNDF 모형은 파머수문학적 가뭄지수를 활용하여 낙동강 유역의 10개 지점을 대상으로 가뭄을 확률적으로 예측하는데 적용되었다. PBNDF 모형의 ROC 분석 결과 ROC 점수가 0.5 이상의 유의한 결과를 나타내 실제 예측 모형으로 활용가능하다는 것을 확인할 수 있었다. 또한, 기존에 개발된 모형(지속성 예측, 베이지안 네트워크 예측 모형)과 평균제곱오차의 제곱근(RMSE), 기술 점수(SS)를 활용하여 비교를 수행하였으며, 그 결과 PBNDF 모형의 RMSE는 상대적으로 낮은 값을 가지며, SS는 약 0.1~0.15 정도 높은 것으로 나타나 예측성능이 향상되었다는 것을 확인할 수 있었다.

키워드
베이지안 네트워크
가뭄 전이
수문학적 가뭄
확률 예측
References
1
Bonaccorso, B., Cancelliere, A., and Rossi, G. (2015). “Probabilistic forecasting of drought class transitions in Sicily (Italy) using standardized precipitation index and North Atlantic oscillation index.” Journal of Hydrology, Vol. 526, pp. 136-150.
2
Changnon Jr., S. A. (1987). Detecting drought conditions in Illinois (Vol. 169). Illinois State Water Survey.
3
Chen, S. T., Yang, T. C., Kuo, C. M., Kuo, C. H., and Yu, P. S. (2013). “Probabilistic drought forecasting in Southern Taiwan using El Niño-Southern Oscillation Index.” Terrestrial, Atmospheric & Oceanic Sciences, Vol. 24, No. 5, pp. 911-924.
4
Cutore, P., Di Mauro, G., and Cancelliere, A. (2009). “Forecasting palmer index using neural networks and climatic indexes.” Journal of Hydrologic Engineering, Vol. 14, No. 6, pp. 588-595.
5
Dey, S., and Stori, J. A. (2005). “A Bayesian network approach to root cause diagnosis of process variations.” International Journal of Machine Tools and Manufacture, Vol. 45, No. 1, pp. 75-91.
6
Eltahir E. A., and Yeh, P. J. F. (1999). “On the asymmetric response of aquifer water level to floods and droughts in Illinois.” Water Resources Research, Vol. 35, pp. 1199-1217.
7
Epstein, E. S., (1969). “A scoring system for probability forecasts of ranked categories” Journal of Applied Meteorology, Vol. 8, No. 6, pp. 985-987.
8
Guttman, N. B., (1991). “A sensitivity analysis of the Palmer hydrologic drought index.” Water Resources Bulletin, Vol. 27, pp. 797-807.
9
Heim, R. R., Jr. (2000). “Drought indices: a review.” Drought: a global assessment, Edited by Wilhite, D. A., Routledge, London, pp. 159-167.
10
Hwang, Y., and Carbone, G. J. (2009). “Ensemble forecasts of drought indices using a conditional residual resampling technique.” Journal of Applied Meteorology and Climatology, Vol. 48, No. 7, pp. 1289-1301.
11
Jacobi, J., Perrone, D., Duncan, L. L., and Hornberger, G. (2013). “A tool for calculating the Palmer drought indices.” Water Resources Research, Vol. 49, No. 9, pp. 6086-6089.
12
Keyantash, J., and Dracup, J. A. (2002). “The quantification of drought: an evaluation of drought indices.” Bulletin of the American Meteorological Society, Vol. 83, No. 8, pp. 1167-1180.
13
Kim, J.-S., Seo G.-S., Jang, H.-W., and Lee, J.-H. (2017). “Correlation analysis between Korean spring drought and large-scale tele-connection patterns for drought forecasting” KSCE Journal of Civil Engineering, Vol. 21, No. 1, pp. 458-466.
14
Kim, T. W., and Valdes, J. B. (2003) “Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks.” Journal of Hydrologic Engineering, Vol. 8, No. 6, pp. 319-328.
15
Kim, Y. O., Lee, J. K., and Palmer, R. N. (2012). “A drought outlook study in Korea.” Hydrological Sciences Journal, Vol. 57, No. 6, pp. 1141-1153.
16
Lee, C. J., and Lee, K. J. (2006). “Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal.” Reliability Engineering & System Safety, Vol. 91, No. 5, pp. 515-532.
17
Lee, D. R. (1999). “Relationship between El Nino/Southern Oscillation and drought in Korea.” Journal of Korea Water Resources Association, Vol. 32, No. 2, pp.111-120.
18
Lee, J. H., Kwon, H. H., Jang, H. W., and Kim, T. W. (2016). “Future changes in drought characteristics under extreme climate change over South Korea.” Advances in Meteorology, Vol. 2016, No. 9164265, pp. 1-19.
19
Madadgar, S., and Moradkhani, H. (2014). “Spatio-temporal drought forecasting within Bayesian networks.” Journal of Hydrology, Vol. 512, pp. 134-146.
20
Madadgar, S., AghaKouchak, A., Shukla, S., Wood, A. W., Cheng, L., Hsu, K. L., and Svoboda, M. (2016). “A hybrid statistical dynamical framework for meteorological drought prediction: Application to the southwestern United States.” Water Resources Research, Vol. 52, No. 7, pp. 5095-5110.
21
Maity, R., Ramadas, M., and Govindaraju, R. S. (2013). “Identification of hydrologic drought triggers from hydroclimatic predictor variables.” Water Resources Research, Vol. 49, No. 7, pp. 4476-4492.
22
Mishra, A. K., and Desai, V. R. (2005). “Drought forecasting using stochastic models.” Stochastic Environmental Research and Risk Assessment, Vol. 19, No. 5, pp. 326-339.
23
Mishra, A. K., and Singh, V. P. (2011). “Drought modeling: a review.” Journal of Hydrology, Vol. 403, No. 1-2, pp. 157-175.
24
Mishra, A., Desai, V., and Singh, V. (2007). “Drought forecasting using a hybrid stochastic and neural network model.” Journal of Hydrologic Engineering, Vol. 12, No. 6, pp. 626-638.
25
Morid, S., Smakhtin, V., and Bagherzadeh, K. (2007). “Drought forecasting using artificial neural networks and time series of drought indices.” International Journal of Climatology, Vol. 27, No. 15, pp. 2103-2111.
26
Mwangi, E., Wetterhall, F., Dutra, E., Di Giuseppe, F., and Pappenberger, F. (2014). “Forecasting droughts in East Africa.” Hydrology and Earth System Sciences, Vol. 18, No. 2, pp. 611-620.
27
Russell, S., and Norvig, P. (1995). Artificial intelligence: a modern approach. Englewood Cliff, Prentice Hall.
28
Santos, J. F., Portela, M. M., and Pulido-Calvo, I. (2014). “Spring drought prediction based on winter NAO and global SST in Portugal.” Hydrological Processes, Vol. 28, No. 3, pp. 1009-1024.
29
Shin, J. Y., Ajmal, M., Yoo, J., and Kim, T.-W. (2016). “A Bayesian network-based probabilistic framework for drought forecasting and outlook.” Advances in Meteorology, Vol. 2016, No. 9472605, pp. 1-10.
30
Sohn, S. J., Min, Y. M., Lee, J. Y., Tam, C. Y., Kang, I. S., Wang, B., Ahn, J. B., and Yamagata, T. (2012). “Assessment of the long- lead probabilistic prediction for the Asian summer monsoon precipitation (1983-2011) based on the APCC multimodel system and a statistical model.” Journal of Geophysical Research, Vol. 117, No. D4, pp. 1-12.
31
Van Koten, C., and Gray, A. R. (2006). “An application of Bayesian network for predicting object-oriented software maintainability.” Information and Software Technology, Vol. 48, No. 1, pp. 59-67.
32
Van Loon A. F. (2015). “Hydrological drought explained.” Wiley Interdisciplinary Reviews: Water, No. 2, pp. 359-392.
33
Yoon, J. H., Mo, K., and Wood, E. F. (2012). “Dynamic-model-based seasonal prediction of meteorological drought over the contiguous United States.” Journal of Hydrometeorology, Vol. 13, No. 2, pp. 463-482.
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 50
  • No :11
  • Pages :769-779
  • Received Date : 2017-06-27
  • Revised Date : 2017-09-29
  • Accepted Date : 2017-09-29
  • DOI :https://doi.org/10.3741/JKWRA.2017.50.11.769
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