Deep learning for predicting the monsoon over the homogeneous regions of India |
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| Authors: | Moumita Saha Pabitra Mitra Ravi S Nanjundiah |
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| Institution: | 1.Department of Computer Science and Engineering,Indian Institute of Technology Kharagpur,Kharagpur,India;2.Divecha Centre for Climate Change,Indian Institute of Science,Bangalore,India;3.Indian Institute of Tropical Meteorology,Pune,India;4.Centre for Atmospheric and Oceanic Sciences,Indian Institute of Science,Bangalore,India |
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| 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. |
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