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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References
Baldi P 2012 Autoencoders, unsupervised learning, and deep architectures; ICML Unsupervised and Transfer Learning 27 37–50.
Cherchi A and Navarra A 2013 Influence of ENSO and of the Indian Ocean Dipole on the Indian summer monsoon variability; Climate Dyn. 41(1) 81–103.
Das P K 1988 The monsoons; National Book Trust, India.
DelSole T and Shukla J 2012 Climate models produce skillful predictions of Indian summer monsoon rainfall; Geophys. Res. Lett. 39(9) L09703.
Gadgil S, Vinayachandran P N, Francis P A and Gadgil S 2004 Extremes of the Indian summer monsoon rainfall, ENSO and equatorial Indian Ocean oscillation; Geophys. Res. Lett. 31(12) L12213.
Gowariker V, Thapliyal V, Kulshrestha S M, Mandal G S, Sen Roy N and Sikka D R 1991 A power regression model for long range forecast of southwest monsoon rainfall over India; Mausam 42(2) 125–130.
Hinton G E and Salakhutdinov R R 2006 Reducing the dimensionality of data with neural networks; Science 313(5786) 504–507.
Hong Y T, Hong B, Lin Q H, Zhu Y X, Shibata Y, Hirota M, Uchida M, Leng X T, Jiang H B and Xu H 2003 Correlation between Indian Ocean summer monsoon and North Atlantic climate during the Holocene; Earth Planet. Sci. Lett. 211(3) 371–380.
Kakade S and Kulkarni A 2016 Prediction of summer monsoon rainfall over India and its homogeneous regions; Meteorol. Appl. 23(1) 1–13.
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Jenne R and Joseph D 1996 The NCEP/NCAR 40-Year Reanalysis Project; Bull. Am. Meteorol. Soc. 77(3) 437–471.
Krishnamurti T N and Bhalme H N 1976 Oscillations of a monsoon system. Part I. Observational aspects; J. Atmos. Sci. 33(10) 1937–1954.
Loh W Y 2008 Classification and regression tree methods; Encyclopedia of statistics in quality and reliability, Ruggeri, (eds) Kenett and Faltin, pp. 315–323.
MATLAB 2012 Statistics and Machine Learning Toolbox; MATLAB version 2012b, The MathWorks Inc., Natick, Massachusetts, US.
Nair A, Mohanty U C and Acharya N 2013 Monthly prediction of rainfall over India and its homogeneous zones during monsoon season: A supervised principal component regression approach on general circulation model products; Theor. Appl. Climatol. 111(1–2) 327–339.
Rajeevan M, Pai D S, Dikshit S K and Kelkar R R 2004 IMD’s new operational models for long-range forecast of southwest monsoon rainfall over India and their verification for 2003; Curr. Sci. 86(3) 422–431.
Rajeevan M, Pai D S, Kumar R A and Lal B 2007 New statistical models for long-range forecasting of southwest monsoon rainfall over India; Climate Dyn. 28(7–8) 813–828.
Saha M, Mitra P and Nanjundiah R S 2016a Autoencoder-based identification of predictors of Indian monsoon; Meteor. Atmos. Phys. 128(5) 613–628.
Saha M, Santara A, Mitra P, Chakraborty A, and Nanjundiah R S 2016b Stacked Autoencoder Based Identification of Monsoon Predictors for the Prediction of the Indian Summer Monsoon; Theor. Appl. Climatol. (under review).
Saha M, Mitra P and Nanjundiah R S 2016c Predictor Discovery for Early-late Indian Summer Monsoon Using Stacked Autoencoder; Procedia Comp. Sc., ICCS 80 565–576.
Saha M and Mitra P 2016 Recurrent neural network based prediction of Indian summer monsoon using global climatic predictors; IJCNN 1523–1529.
Sinha P, Mohanty U C, Kar S C, Dash S K, Robertson A W and Tippett M K 2013 Seasonal prediction of the Indian summer monsoon rainfall using canonical correlation analysis of the NCMRWF global model products; Int. J. Climatol. 33(7) 1601–1614.
Wang B, Xiang B, Li J, Webster P J, Rajeevan M N, Liu J and Ha K 2015 Rethinking Indian monsoon rainfall prediction in the context of recent global warming; Nature Comm. 6:7154, doi:10.1038/ncomms8154.
Acknowledgements
We gratefully acknowledge department of Computer Science and Engineering at Indian Institute of Technology Kharagpur, for providing all the supports for carrying out the work. We also deeply appreciate the support of Centre for Atmospheric and Oceanic Sciences at Indian Institute of Science Bangalore. Finally, we are thankful to our reviewers for their constructive suggestions.
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Saha, M., Mitra, P. & Nanjundiah, R.S. Deep learning for predicting the monsoon over the homogeneous regions of India. J Earth Syst Sci 126, 54 (2017). https://doi.org/10.1007/s12040-017-0838-7
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DOI: https://doi.org/10.1007/s12040-017-0838-7