Prediction of Rainfall in Australia Using Machine Learning

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Sarasa Cabezuelo, Antonio (2022) Prediction of Rainfall in Australia Using Machine Learning. Information, 13 (4). p. 163. ISSN 2078-2489

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Official URL: https://doi.org/10.3390/info13040163




Abstract

Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic models are used that offer predictions with a margin of error, so that in many cases they are not very good. Due to the aforementioned conditions, the use of machine learning algorithms can serve to improve predictions. This article describes an exploratory study of the use of machine learning to make predictions about the phenomenon of rain. To do this, a set of data was taken as an example that describes the measurements gathered on rainfall in the main cities of Australia in the last 10 years, and some of the main machine learning algorithms were applied (knn, decision tree, random forest, and neural networks). The results show that the best model is based on neural networks.


Item Type:Article
Uncontrolled Keywords:machine learning; rain forecast; meteorological phenomena; knn; decision tree; random forest; neural networks
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Statistics
Medical sciences > Biology > Environment
ID Code:74854
Deposited On:30 Sep 2022 11:05
Last Modified:03 Oct 2022 07:05

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