Rainfall Prediction: A Deep Learning Approach
| Autor: | Victor Sanchez-Anguix, Emilcy Juliana Hernandez, Néstor Duque, Vicente Julián, Javier Palanca |
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| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network Emergency management business.industry Computer science Deep learning 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Autoencoder Task (project management) Multilayer perceptron 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Precipitation business computer 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319320335 HAIS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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