The Improvement of Missing Rainfall Data Estimation During Rainy Season at Ampang Station

  • Authors

    • Nurul Aishah Rahman
    • Sayang Mohd Deni
    • Norazan Mohamed Ramli
    • Norshahida Shaadan
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.18.21912
  • Daily rainfall data, Generalized linear model, Imputation method, Missing data.

  • Abstract

    The availability of rainfall data plays a significant role in water related sectors. The presence of missing values could produce biasness in the results of data analysis. Several methods have been used to estimate the missing values such as simple arithmetic average, normal ratio method, inverse distance weighting method, correlation coefficient weighting method and geographical coordinate. However, the estimated values produced by the imputation methods had been used scarcely considered rainfall pattern during the estimation process. To fill the gap, the generalized linear model (GLM) was used to assess the performance of the imputation methods at a target station namely Ampang station. The experimentation was conducted using real data set for the period from 1975 to 2014. Neighbouring rainfall stations with 25km and 35km away from the target station and the duration during rainy and non-rainy period were considered in assessing the capability of the model. This study aims to assess the performance of GLM methods in comparison with the current existing techniques in estimating the missing values. Based on the mean absolute error and root mean squared error, the results have shown that the application of GLM able to produce better and accurate rainfall data estimation.

     

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  • How to Cite

    Aishah Rahman, N., Mohd Deni, S., Mohamed Ramli, N., & Shaadan, N. (2018). The Improvement of Missing Rainfall Data Estimation During Rainy Season at Ampang Station. International Journal of Engineering & Technology, 7(4.18), 204-212. https://doi.org/10.14419/ijet.v7i4.18.21912

    Received date: 2018-11-27

    Accepted date: 2018-11-27

    Published date: 2018-11-27