Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/11933
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dc.contributor.authorVathsala, H.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2020-03-31T08:35:55Z-
dc.date.available2020-03-31T08:35:55Z-
dc.date.issued2017
dc.identifier.citationTheoretical and Applied Climatology, 2017, Vol.130, 43862, pp.19-33en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11933-
dc.description.abstractThis paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969 2005). 2016, Springer-Verlag Wien.en_US
dc.titleLong-range prediction of Indian summer monsoon rainfall using data mining and statistical approachesen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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