Sequential Pattern Discovery for Weather Prediction Problem

  • Almahdi Alshareef
  • Azuraliza Abu Bakar
  • Abdul Razak Hamdan
  • Sharifah Mastura Syed Abdullah
  • Othman Jaafar
Part of the Studies in Computational Intelligence book series (SCI, volume 647)


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.


Episodes mining Sequential patterns Pattern discovery 



This project is under the Climate Informatics Research Project with Exploratory Research Grant Scheme ERGS/1/2012/STG07/UKM/01/1 and FRGS/1/2014/ICT02/UKM/01/2 Ministry of Higher Education Malaysia.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Almahdi Alshareef
    • 1
  • Azuraliza Abu Bakar
    • 1
  • Abdul Razak Hamdan
    • 1
  • Sharifah Mastura Syed Abdullah
    • 2
  • Othman Jaafar
    • 2
  1. 1.Center for Artificial Intelligence TechnologyFaculty of Information Science and TechnologyBangiMalaysia
  2. 2.Institute of Climate ChangeUniversiti Kebangsaan MalaysiaBangiMalaysia

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