Spatial estimation of average daily precipitation using multiple linear regression by using topographic and wind speed variables in tropical climate

    Mohd Talha Anees Affiliation
    ; Khiruddin Abdullah Affiliation
    ; M. N. M. Nawawi Affiliation
    ; Nik Norulaini Nik Ab Rahman Affiliation
    ; Abd. Rahni Mt. Piah Affiliation
    ; M. I. Syakir Affiliation
    ; Mohammad Muqtada Ali Khan Affiliation
    ; Abdul Kadir Mohd. Omar Affiliation


Complex topography and wind characteristics play important roles in rising air masses and in daily spatial distribution of the precipitations in complex region. As a result, its spatial discontinuity and behaviour in complex areas can affect the spatial distribution of precipitation. In this work, a two-fold concept was used to consider both spatial discontinuity and topographic and wind speed in average daily spatial precipitation estimation using Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR) in tropical climates. First, wet and dry days were identified by the two methods. Then the two models based on MLR (Model 1 and Model 2) were applied on wet days to estimate the precipitation using selected predictor variables. The models were applied for month wise, season wise and year wise daily averages separately during the study period. The study reveals that, Model 1 has been found to be the best in terms of categorical statistics, R2 values, bias and special distribution patterns. However, it was found that sets of different predictor variables dominates in different months, seasons and years. Furthermore, necessities of other data for further enhancement of the results were suggested.

Keyword : inverse distance weighting, interpolation, multiple linear regression, precipitation occurrence, spatial distribution, wind speed

How to Cite
Anees, M. T., Abdullah, K., Nawawi, M. N. M., Ab Rahman, N. N. N., Mt. Piah, A. R., Syakir, M. I., Ali Khan, M. M., & Mohd. Omar, A. K. (2018). Spatial estimation of average daily precipitation using multiple linear regression by using topographic and wind speed variables in tropical climate. Journal of Environmental Engineering and Landscape Management, 26(4), 299-316.
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Nov 15, 2018
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