Browse > Article
http://dx.doi.org/10.3741/JKWRA.2013.46.12.1249

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

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)
Publication Information
Journal of Korea Water Resources Association / v.46, no.12, 2013 , pp. 1249-1263 More about this Journal
Abstract
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.
Keywords
artificial neural network; drought; multi layer perceptron; SPI; forecasting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Jeong, H.J., Lee, S.J., and Lee, H.K. (2002). "Water Quality Forecasting of Chungju Lake Using Artificial Neural network Algorithm." Korean Environmental Science Society, Vol. 11, No. 3, pp. 201-207.   과학기술학회마을   DOI   ScienceOn
2 Kang, B.S., and Lee, B.K. (2011). "Application of Artificial Neural Network to Improve Qauntitative Precipitation Forecasts of Meso-scale Numerical Weather Prediction." Journal of Korea Water Resources Association, Vol. 44, No. 2, pp. 97-107.   과학기술학회마을   DOI   ScienceOn
3 Kim, C.J., Park, M.J., and Lee, J.H. (2013). "Analysis of climate change impacts on the spatial and frequency patterns of drought using a potential drought hazard mapping approach." International Journal of Climatology, Published in Online, DOI=10.1002/joc.3666.   DOI   ScienceOn
4 Kwon, H.H., Moon, J.W., Song, H.S., and Moon, Y.I. (2009). "Climate Information and GCMs Seasonal Forecasts based Short-term Forecasts for Drought." Conference of Korea Water Resources Association, pp. 1186-1190.
5 Mckee, T.B., Doesken, N.J., and Kleist, J. (1993). The relationship of drought frequency and duration of time scales. 8th Conference on Applied Climatology, Jan., Anaheim, CA, pp. 179-184.
6 Mckee, T.B., Doesken, N.J., and Kleist, J. (1995). Drought monitering with multiple time scales preprints. 9th Conference on Applied Climatology, 15-20 Janiary, Dallas, TX, pp. 233-236.
7 Mishra, A.K., and Desai, V.R. (2006). "Drought forecasting using feed-forward recursive neural network." Eclolgical Modelling, Vol. 198, Issue 1-2, pp. 127-138.   DOI   ScienceOn
8 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.   DOI   ScienceOn
9 Paulo, A.A., Ferreira, E., Coelho, C., and Pereira, L.S. (2005). "Drought class transition analysis through Markov and Loglinear models, an approach to early warning." Agricultural Water Management, Vol. 77, pp. 59-81.   DOI   ScienceOn
10 Seo, J.W. (2011). Analysis on the Statistical Characteristics of Drought in Korea using SPI and PDSI . M.S. dissertation. University of Kyung Hee, Seoul, pp. 44
11 Souhaib B.T., and Rob, J.H. (2012). Recursive and Direct multi-step forecasting: the best of both worlds. Ph.D. dissertation, University of Monash, Victoria, Clayton, Australia, pp. 19-12.
12 Bacanil, U.G., Firat, M., and Dikbas, F. (2009). "Adaptive Neuro-Fuzzy Inference System for drought forecastiong." Stochastic Environmental Research and Risk Assessment, Vol. 23, No. 8, pp. 1143-1154.   DOI
13 Belayneh, A., and Adamowski, J. (2012). "Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression." Applied computational Intelligence and Soft Computing, Vol. 2012 No. 6, DOI=10.1155/2012/794061.   DOI
14 Chen, J., Huang, Z., and Jin, Q. (2012). "SPI-based drought characteristics analysis and prediction for Xiqiao Station in Yunnan Province, China." Disaster Advances, Vol 5, pp. 396-407
15 Ghosh, S., and Mujumdar, P.P. (2007). "Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment."Water Resources Research, Vol. 43, No 7, W07405-W07406.