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2016 | OriginalPaper | Chapter

Sequential Pattern Discovery for Weather Prediction Problem

Authors : Almahdi Alshareef, Azuraliza Abu Bakar, Abdul Razak Hamdan, Sharifah Mastura Syed Abdullah, Othman Jaafar

Published in: Emerging Trends and Advanced Technologies for Computational Intelligence

Publisher: Springer International Publishing

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Abstract

This study proposes the Sequential Pattern Discovery algorithms to solve weather prediction problem. A novel weather pattern discovery framework is presented to highlight the important processes in this work. Two algorithms are employed; namely episodes and sequential pattern mining algorithms. The episodes mining algorithm is introduced to find frequent episodes in rainfall sequences and sequential pattern mining algorithm to find relationship of patterns between weather stations. Real data are collected from ten rainfall stations of Selangor State, Malaysia. The sequential pattern algorithm is applied to extract the relationship between ten rainfall stations in 33 years periods of time. The patterns are evaluated experimentally by support and confidence values while some specific rules are mapped to the location of stations and analysed for more verification. The proposed study produces valuable patterns of weather and preserves important knowledge for weather prediction.

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Metadata
Title
Sequential Pattern Discovery for Weather Prediction Problem
Authors
Almahdi Alshareef
Azuraliza Abu Bakar
Abdul Razak Hamdan
Sharifah Mastura Syed Abdullah
Othman Jaafar
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-33353-3_12

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