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http://dx.doi.org/10.7465/jkdi.2017.28.5.1125

Drought index forecast using ensemble learning  

Jeong, Jihyeon (Department of Statistics, Kyungpook National University)
Cha, Sanghun (Department of Statistics, Kyungpook National University)
Kim, Myojeong (School of Architectural, Civil, Environment, and Energy Engineering, Kyungpook National University)
Kim, Gwangseob (School of Architectural, Civil, Environment, and Energy Engineering, Kyungpook National University)
Lim, Yoon-Jin (National Institute of Meteorological Sciences)
Lee, Kyeong Eun (Department of Statistics, Kyungpook National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.5, 2017 , pp. 1125-1132 More about this Journal
Abstract
In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.
Keywords
Additive model; drought forecast; ensemble learning; stochastic gradient descent;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Clarke, B. (2003). Comparing bayes model averaging and stacking when model approximation error cannot be ignored. Journal of Machine Learning Research, 4, 683-712.
2 Friedman, J. H. and Stuetzle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823.   DOI
3 Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35, 73-101.   DOI
4 Kwak, S. (2014). Comparison of ensemble pruning methods using Lasso-bagging and WAVE-bagging. Journal of the Korean Data & Information Science Society, 25, 1371-1383.   DOI
5 Robbins, H. and Monro, S. (1951). A Stochastic approximation method. The Annals of Mathematical Statistics, 22, 400-407.   DOI
6 Wolpert, D. H. (1999). An efficient method to estimate bagging's generalization error. Machine Learning Journal, 35, 41-55.   DOI
7 Yoon, S. (2016). Generating high resolution of daily mean temperature using statistical models. Journal of the Korean Data & Information Science Society, 27, 1215-1224.   DOI