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Advances in Meteorology
Volume 2016 (2016), Article ID 9031625, 12 pages
Research Article

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

1Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India
2Center for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology, Kharagpur, India

Received 5 July 2016; Revised 17 August 2016; Accepted 24 August 2016

Academic Editor: Anthony R. Lupo

Copyright © 2016 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.


Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.