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      https://www.ias.ac.in/article/fulltext/jess/126/04/0054

    • Keywords

       

      Feature learning; stacked autoencoder; monsoon predictor; ensemble of regression trees; regional Indian summer monsoon.

    • Abstract

       

      Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

    • Author Affiliations

       

      Moumita Saha1 2 Pabitra Mitra1 Ravi S Nanjundiah3 4 2

      1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.
      2. Present address: Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore 560 012, India.
      3. Divecha Centre for Climate Change, Indian Institute of Science, Bangalore 560 012, India.
      4. Indian Institute of Tropical Meteorology, Pune, Maharashtra 411008, India.
    • Dates

       
  • Journal of Earth System Science | News

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