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Advances in Meteorology
Volume 2015 (2015), Article ID 329835, 12 pages
http://dx.doi.org/10.1155/2015/329835
Research Article

Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon

1Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, Paschim Medinipur, West Bengal 721302, India
2Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, Paschim Medinipur, West Bengal 721302, India

Received 2 January 2015; Revised 31 March 2015; Accepted 3 April 2015

Academic Editor: Xiaolong Jia

Copyright © 2015 Moumita Saha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Indian monsoon is an important climatic phenomenon and a global climatic marker. Both statistical and numerical prediction schemes for Indian monsoon have been widely studied in literature. Statistical schemes are mainly based on regression or neural networks. However, the variability of monsoon is significant over the years and a single model is often inadequate. Meteorologists revise their models on different years based on prevailing global climatic incidents like El-Niño. These indices often have degree of severity associated with them. In this paper, we cluster the monsoon years based on their fuzzy degree of associativity to these climatic event patterns. Next, we develop individual prediction models for the year clusters. A weighted ensemble of these individual models is used to obtain the final forecast. The proposed method performs competitively with existing forecast models.