Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon

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...

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Main Authors: Moumita Saha, Pabitra Mitra, Arun Chakraborty
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2015/329835
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spelling doaj-77995af2fe964f3d80f232a0aaba3e282020-11-24T22:59:11ZengHindawi LimitedAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/329835329835Fuzzy Clustering-Based Ensemble Approach to Predicting Indian MonsoonMoumita Saha0Pabitra Mitra1Arun Chakraborty2Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, Paschim Medinipur, West Bengal 721302, IndiaDepartment of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, Paschim Medinipur, West Bengal 721302, IndiaCentre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, Paschim Medinipur, West Bengal 721302, IndiaIndian 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.http://dx.doi.org/10.1155/2015/329835
collection DOAJ
language English
format Article
sources DOAJ
author Moumita Saha
Pabitra Mitra
Arun Chakraborty
spellingShingle Moumita Saha
Pabitra Mitra
Arun Chakraborty
Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon
Advances in Meteorology
author_facet Moumita Saha
Pabitra Mitra
Arun Chakraborty
author_sort Moumita Saha
title Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon
title_short Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon
title_full Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon
title_fullStr Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon
title_full_unstemmed Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon
title_sort fuzzy clustering-based ensemble approach to predicting indian monsoon
publisher Hindawi Limited
series Advances in Meteorology
issn 1687-9309
1687-9317
publishDate 2015-01-01
description 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.
url http://dx.doi.org/10.1155/2015/329835
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AT pabitramitra fuzzyclusteringbasedensembleapproachtopredictingindianmonsoon
AT arunchakraborty fuzzyclusteringbasedensembleapproachtopredictingindianmonsoon
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