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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Online Access: | http://dx.doi.org/10.1155/2015/329835 |
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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 |
work_keys_str_mv |
AT moumitasaha fuzzyclusteringbasedensembleapproachtopredictingindianmonsoon AT pabitramitra fuzzyclusteringbasedensembleapproachtopredictingindianmonsoon AT arunchakraborty fuzzyclusteringbasedensembleapproachtopredictingindianmonsoon |
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1725645420417253376 |