Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon
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 monso...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2016/9031625 |
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| author | Moumita Saha Arun Chakraborty Pabitra Mitra |
| author_facet | Moumita Saha Arun Chakraborty Pabitra Mitra |
| author_sort | Moumita Saha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0daa38899cc947dab4e0fb4293b9cd1c |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-0daa38899cc947dab4e0fb4293b9cd1c2025-08-20T03:54:24ZengWileyAdvances in Meteorology1687-93091687-93172016-01-01201610.1155/2016/90316259031625Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer MonsoonMoumita Saha0Arun Chakraborty1Pabitra Mitra2Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, IndiaCenter for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology, Kharagpur, IndiaDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, IndiaForecasting 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.http://dx.doi.org/10.1155/2016/9031625 |
| spellingShingle | Moumita Saha Arun Chakraborty Pabitra Mitra Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon Advances in Meteorology |
| title | Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon |
| title_full | Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon |
| title_fullStr | Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon |
| title_full_unstemmed | Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon |
| title_short | Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon |
| title_sort | predictor year subspace clustering based ensemble prediction of indian summer monsoon |
| url | http://dx.doi.org/10.1155/2016/9031625 |
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