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Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming

Cramer, Sam and Kampouridis, Michael and Freitas, Alex A. and Alexandridis, Antonis (2016) Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming. In: 2015 IEEE Symposium Series on Computational Intelligence. IEEE, pp. 711-718. ISBN 978-1-4799-7560-0. (doi:10.1109/SSCI.2015.108)

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https://dx.doi.org/10.1109/SSCI.2015.108

Abstract

Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in the prediction of rainfall for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we outline a new methodology to be carried out by predicting rainfall with Genetic Programming (GP). This is the first time in the literature that GP is used within the context of rainfall derivatives. We have created a new tailored GP to this problem domain and we compare the performance of the GP and MCRP on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform MCRP, which acts as a benchmark. Results indicate that in general GP significantly outperforms MCRP, which is the dominant approach in the literature.

Item Type: Book section
DOI/Identification number: 10.1109/SSCI.2015.108
Uncontrolled keywords: contracts; meteorology; cities and towns; pricing; predictive models; genetic programming; context
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Michael Kampouridis
Date Deposited: 16 Sep 2015 21:02 UTC
Last Modified: 25 Sep 2019 11:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50522 (The current URI for this page, for reference purposes)
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