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An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Cramer, Sam, Kampouridis, Michael, Freitas, Alex A., Alexandridis, Antonis (2017) An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications, 85 . pp. 169-181. ISSN 0957-4174. (doi:10.1016/j.eswa.2017.05.029)

Abstract

Regression problems provide some of the most challenging research opportunities in the area of machine learning, and more broadly intelligent systems, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. This work’s main impact is to show the benefit machine learning algorithms, and more broadly intelligent systems have over the current state-of-the-art techniques for rainfall prediction within rainfall derivatives. We apply and compare the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours. To assist in the extensive evaluation, we run tests using the rainfall time series across data sets for 42 cities, with very diverse climatic features. This thorough examination shows that the machine learning methods are able to outperform the current state-of-the-art. Another contribution of this work is to detect correlations between different climates and predictive accuracy. Thus, these results show the positive effect that machine learning-based intelligent systems have for predicting rainfall based on predictive accuracy and with minimal correlations existing across climates.

Item Type: Article
DOI/Identification number: 10.1016/j.eswa.2017.05.029
Uncontrolled keywords: weather derivatives, rainfall, machine learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Faculties > Social Sciences > Kent Business School
Depositing User: Michael Kampouridis
Date Deposited: 16 May 2017 13:20 UTC
Last Modified: 09 Jul 2019 09:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/61742 (The current URI for this page, for reference purposes)
Kampouridis, Michael: https://orcid.org/0000-0003-0047-7565
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