ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Deep learning for predicting the monsoon over the homogeneous regions of India

Saha, Moumita and Mitra, Pabitra and Nanjundiah, Ravi S (2017) Deep learning for predicting the monsoon over the homogeneous regions of India. In: JOURNAL OF EARTH SYSTEM SCIENCE, 126 (4).

[img] PDF
Jo_Ea_Sy_Sc_126-4_2017.pdf - Published Version

Download (3MB)
Official URL: http://dx.doi.org/10.1007/s12040-017-0838-7

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.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the INDIAN ACAD SCIENCES, C V RAMAN AVENUE, SADASHIVANAGAR, P B #8005, BANGALORE 560 080, INDIA
Department/Centre: Division of Mechanical Sciences > Divecha Centre for Climate Change
Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
Depositing User: Id for Latest eprints
Date Deposited: 29 Jul 2017 10:17
Last Modified: 29 Jul 2017 10:17
URI: http://eprints.iisc.ac.in/id/eprint/57521

Actions (login required)

View Item View Item