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Neural Network Algorithm Variants for Malaysian Weather Prediction

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Soft Computing Applications and Intelligent Systems (M-CAIT 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 378))

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Abstract

This paper studies the performance of a newly prepared time-series rainfall data by a previous researcher. The issues related are; (i) the large amount of data, and (ii) the accuracy of the prediction. In this study, the data set was obtained from Institute of Climate Change UKM, pre-processed using improved Symbolic Aggregate approximation (iSAX) and verified by experts. Five neural network algorithms were tested with the data set, namely standard Back-Propagation (BPNN), Back-Propagation with Momentum (BP with Mom), Quick-Propagation (QuickProp), Genetic Algorithm with neural network (GA-NN) and Particle Swarm Optimization with neural network (PSO-NN). The performances of these engines were measured according to the accuracy of prediction and the training time taken. The experimental results showed that while standard BPNN and PSO-NN achieved about the same accuracy prediction, PSO-NN is considered to be better as it showed a faster training time with acceptable prediction accuracy.

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Author information

Authors and Affiliations

  1. Faculty of Technology and Information Science, Universiti Kebangsaan Malaysia, 46300, Bangi, Selangor, Malaysia

    Siti Nur Kamaliah Kamarudin & Azuraliza Abu Bakar

Authors
  1. Siti Nur Kamaliah Kamarudin
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  2. Azuraliza Abu Bakar
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Editor information

Editors and Affiliations

  1. Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Shahrul Azman Noah

  2. Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor D. E, Malaysia

    Azizi Abdullah

  3. Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Haslina Arshad , Zulaiha Ali Othman  & Zalinda Othman ,  & 

  4. School of Computer Science, FTSM, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Azuraliza Abu Bakar

  5. Pattern Recognition Research Group, CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Shahnorbanun Sahran

  6. Faculty of Information Science & IT, National University of Malaysia, 43600, Bangi, Selangor, Malaysia

    Nazlia Omar

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© 2013 Springer-Verlag Berlin Heidelberg

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Kamarudin, S.N.K., Abu Bakar, A. (2013). Neural Network Algorithm Variants for Malaysian Weather Prediction. In: Noah, S.A., et al. Soft Computing Applications and Intelligent Systems. M-CAIT 2013. Communications in Computer and Information Science, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40567-9_11

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  • DOI: https://doi.org/10.1007/978-3-642-40567-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40566-2

  • Online ISBN: 978-3-642-40567-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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