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  1. Muhammad Fauzee Hamdan, Shariffah Suhaila Syed Jamaludin, Abdul Aziz Jemain
    MATEMATIKA, 2018;34(101):167-177.
    MyJurnal
    Rainfall is an interesting phenomenon to investigate since it is directly related
    to all aspects of life on earth. One of the important studies is to investigate and under-
    stand the rainfall patterns that occur throughout the year. To identify the pattern, it
    requires a rainfall curve to represent daily observation of rainfall received during the year.
    Functional data analysis methods are capable to convert discrete data intoa function that
    can represent the rainfall curve and as a result, try to describe the hidden patterns of the
    rainfall. This study focused on the distribution of daily rainfall amount using functional
    data analysis. Fourier basis functions are used for periodic rainfall data. Generalized
    cross-validation showed 123 basis functions were sufficient to describe the pattern of daily
    rainfall amount. North and west areas of the peninsula show a significant bimodal pattern
    with the curve decline between two peaks at the mid-year. Meanwhile,the east shows uni-
    modal patterns that reached a peak in the last three months. Southern areas show more
    uniform trends throughout the year. Finally, the functional spatial method is introduced
    to overcome the problem of estimating the rainfall curve in the locations with no data
    recorded. We use a leave one out cross-validation as a verification method to compare
    between the real curve and the predicted curve. We used coefficient of basis functions
    to get the predicted curve. It was foundthatthe methods ofspatial prediction can match
    up with theexistingspatialpredictionmethodsin terms of accuracy,but it isbetterasthe new
    approach provides a simpler calculation.
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