Predictor Discovery for Early-late Indian Summer Monsoon Using Stacked Autoencoder | Zendy

Moumita Saha | Zendy; Pabitra Mitra | Zendy; Ravi S. Nanjundiah | Zendy

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Predictor Discovery for Early-late Indian Summer Monsoon Using Stacked Autoencoder

Author(s) -

Moumita Saha,

Pabitra Mitra,

Ravi S. Nanjundiah

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.05.337

Subject(s) - autoencoder , monsoon , computer science , climatology , artificial neural network , tree (set theory) , regression , artificial intelligence , geology , statistics , mathematics , mathematical analysis

Indian summer monsoon has distinct behaviors in its early and late phase. The influencing climatic factors are also different. In this work we aim to predict the national rainfall in these phases. The predictors used by the forecast models are discovered using a stacked autoencoder deep neural network. A fitted regression tree is used as the forecast model. A superior accuracy to state of art method is achieved. We also observe that the late monsoon can be predicted with higher accuracy than early monsoon rainfall

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