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Author: search.filters.author.Vathsala, H.Subject: search.filters.subject.statistical analysis

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    Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches
    (Elsevier Ltd, 2017) Vathsala, H.; Koolagudi, S.G.
    In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good. © 2016 Elsevier Ltd
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    Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches
    (Springer-Verlag Wien michaela.bolli@springer.at, 2017) Vathsala, H.; Koolagudi, S.G.
    This 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.