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2016 | OriginalPaper | Chapter

Rainfall Prediction: A Deep Learning Approach

Authors : Emilcy Hernández, Victor Sanchez-Anguix, Vicente Julian, Javier Palanca, Néstor Duque

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

Previous work has shown that the prediction of meteorological conditions through methods based on artificial intelligence can get satisfactory results. Forecasts of meteorological time series can help decision-making processes carried out by organizations responsible of disaster prevention. We introduce an architecture based on Deep Learning for the prediction of the accumulated daily precipitation for the next day. More specifically, it includes an autoencoder for reducing and capturing non-linear relationships between attributes, and a multilayer perceptron for the prediction task. This architecture is compared with other previous proposals and it demonstrates an improvement on the ability to predict the accumulated daily precipitation for the next day.

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Footnotes
1
This threshold was obtained by talking with domain experts.
 
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Metadata
Title
Rainfall Prediction: A Deep Learning Approach
Authors
Emilcy Hernández
Victor Sanchez-Anguix
Vicente Julian
Javier Palanca
Néstor Duque
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32034-2_13

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