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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/329835 |
| Summary: | 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. |
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| ISSN: | 1687-9309 1687-9317 |