search for




 

Drought index forecast using ensemble learning
Journal of the Korean Data & Information Science Society 2017;28:1125-32
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Jihyeon Jeong1 · Sanghun Cha2 · Myojeong Kim3 · Gwangseob Kim4 · Yoon-Jin Lim5 · Kyeong Eun Lee6

126Department of Statistics, Kyungpook National University
34School of Architectural, Civil, Environment, and Energy Engineering, Kyungpook National University
5National Institute of Meteorological Sciences
Correspondence to: Kyeong Eun Lee
Associate Professor, Dept. of Statistics, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea. E-mail: artlee@knu.ac.kr
Received September 4, 2017; Revised September 20, 2017; Accepted September 20, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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