Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

Saha, M and Santara, A and Mitra, P and Chakraborty, A and Nanjundiah, RS (2020) Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model. In: International Journal of Forecasting .

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Abstract

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8 error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.

Item Type:	Journal Article
Publication:	International Journal of Forecasting
Publisher:	Elsevier B.V.
Additional Information:	The copyright of this article belongs to Elsevier B.V.
Department/Centre:	Division of Mechanical Sciences > Divecha Centre for Climate Change Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
Date Deposited:	17 Aug 2020 07:49
Last Modified:	17 Aug 2020 07:49
URI:	http://eprints.iisc.ac.in/id/eprint/66326

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