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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2019: Advances and Trends in Artificial Intelligence. From Theory to Practice pp 27–38Cite as

Infilling Missing Rainfall and Runoff Data for Sarawak, Malaysia Using Gaussian Mixture Model Based K-Nearest Neighbor Imputation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

Hydrologists are often encountered problem of missing values in a rainfall and runoff database. They tend to use the normal ratio or distance power method to deal with the problem of missing data in the rainfall and runoff database. However, this method is time consuming and most of the time, it is less accurate. In this paper, two neighbor-based imputation methods namely K-nearest neighbor (KNN) and Gaussian mixture model based KNN imputation (GMM-KNN) were explored for gap filling the missing rainfall and runoff database. Different percentage of missing data entries were inserted randomly into the database such as 2%, 5%, 10%, 15% and 20% of missing data. Pros and cons of these two methods were compared and discussed. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak, East Malaysia. It is observed that the GMM-KNN imputation method results in the best estimation accuracy for the missing rainfall and runoff database.

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Acknowledgments

The authors sincerely acknowledge the Department of Irrigation and Drainage (DID), Sarawak, Malaysia for providing the rainfall and runoff data in this study. The authors wish to thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876 and the Fundamental Research Grant Scheme (FRGS) Vot 5F073 supported under Ministry of Education Malaysia for the completion of the research. The works were also supported by the SPEV project, University of Hradec Kralove, FIM, Czech Republic (ID: 2102–2019). We are also grateful for the support of Ph.D. student Sebastien Mambou in consultations regarding application aspects.

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Authors and Affiliations

  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Po Chan Chiu & Ali Selamat

  2. MagicX (Media and Games Center of Excellence), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Po Chan Chiu & Ali Selamat

  3. Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia

    Po Chan Chiu

  4. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  5. Faculty of Informatics and Management, University of Hradec Kralove, Rokitanského 62, 500 03, Hradec Kralove, Czech Republic

    Ali Selamat & Ondrej Krejcar

Authors
  1. Po Chan Chiu
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  2. Ali Selamat
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  3. Ondrej Krejcar
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Corresponding author

Correspondence to Po Chan Chiu .

Editor information

Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

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Chiu, P.C., Selamat, A., Krejcar, O. (2019). Infilling Missing Rainfall and Runoff Data for Sarawak, Malaysia Using Gaussian Mixture Model Based K-Nearest Neighbor Imputation. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-22999-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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