Rainfall Prediction: A Deep Learning Approach

Emilcy Hernandez, Victor Sanchez-Anguix, Vicente Julian, Javier Palanca, Nestor Duque

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

54 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationInternational Conference on Hybrid Artificial Intelligence Systems
EditorsFrancisco Martínez-Álvarez, Alicia Troncoso, Héctor Quintián, Emilio Corchado
Place of PublicationSwitzerland
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)978-3-319-32034-2
ISBN (Print)978-3-319-32033-5
Publication statusPublished - 14 Apr 2016
EventInternational Conference on Hybrid Artificial Intelligence Systems - Saville, Spain
Duration: 18 Apr 201620 Apr 2016
Conference number: 11


ConferenceInternational Conference on Hybrid Artificial Intelligence Systems
Abbreviated titleHAIS 2016
Internet address


  • Artificial neural networks
  • Deep learning
  • Meteorological data
  • Rainfall prediction


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