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A Study on Bayesian Principal Component Analysis for Addressing Missing Rainfall Data

Author

Listed:
  • Wai Yan Lai

    (Swinburne University of Technology Sarawak Campus)

  • K. K. Kuok

    (Swinburne University of Technology Sarawak Campus)

Abstract

This paper proposed the application of Bayesian Principal Component Analysis (BPCA) algorithm to address the issue of missing rainfall data in Kuching City. The experiment was conducted using six different combinations of rainfall data from different neighbouring rainfall stations at different missing data entries (1%, 5%, 10%, 15%, 20%, 25% and 30% of missing data entries). The performance of BPCA model in reconstructing the missing data was examined with respect to Bias (Bs), Efficiency (E) and Root Mean Square Error (RMSE). The reliability and robustness of BPCA was confirmed by comparing its performance with K-Nearest Neighbour (KNN) imputation model. The results support the addition of data from neighbouring rainfall stations to improve the imputation accuracy.

Suggested Citation

  • Wai Yan Lai & K. K. Kuok, 2019. "A Study on Bayesian Principal Component Analysis for Addressing Missing Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2615-2628, June.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:8:d:10.1007_s11269-019-02209-8
    DOI: 10.1007/s11269-019-02209-8
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    Cited by:

    1. Thakolpat Khampuengson & Wenjia Wang, 2023. "Novel Methods for Imputing Missing Values in Water Level Monitoring Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 851-878, January.
    2. Rahman, Khalil Ur & Ejaz, Nuaman & Shang, Songhao & Balkhair, Khaled S. & Alghamdi, Khalid Mohammad & Zaman, Kifayat & Khan, Mahmood Alam & Hussain, Anwar, 2024. "A robust integrated agricultural drought index under climate and land use variations at the local scale in Pakistan," Agricultural Water Management, Elsevier, vol. 295(C).
    3. Xin Jing & Jungang Luo & Jingmin Wang & Ganggang Zuo & Na Wei, 2022. "A Multi-imputation Method to Deal With Hydro-Meteorological Missing Values by Integrating Chain Equations and Random Forest," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1159-1173, March.

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