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Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches

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Abstract

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).

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Acknowledgments

We thank Prof. Ravi Nanjundiah, CAOS, IISc and Dr. Gayatri Vani Kanchibhotla, HPC-S&EA, CDAC, Pune for their valuable inputs given during the current work.

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Authors and Affiliations

  1. Center for Development of Advanced Computing (CDAC), 6th floor, Visveswaraiah center, Dr. B. R. Ambedkar Road, Bangalore, 560001, Karnataka, India

    Vathsala H

  2. National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, Karnataka, India

    Shashidhar G. Koolagudi

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  1. Vathsala H
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  2. Shashidhar G. Koolagudi
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Correspondence to Vathsala H.

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H, V., Koolagudi, S.G. Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches. Theor Appl Climatol 130, 19–33 (2017). https://doi.org/10.1007/s00704-016-1862-2

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