Location Weight of GSTAR Model for High Variability of Rainfall Data

 
 
 
  • Abstract
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  • References
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  • Abstract


    The use of location weights in the spatio temporal models took part in the accuracy of the model. The weights that often used are uniform weight, inverse distance weight, and normalized cross-correlation weight. These location weights consider the closeness between locations. For data that have high degree of variability, the use of these location weights becomes less relevant. Therefore, it is necessary to consider variability aspects of observational data as the location weights, that is the normalize ratio variance weight. The study was conducted to develop GSTAR-SUR model with normalize ratio variance weight and its application on rainfall data. The data used is ten daily rainfall data in the region Blimbing, Singosari, Karangploso, Dau, and Wagir. Based on the results of the study, indicated that the GSTAR-SUR ((1) (1,2,3,12,36) model that used ratio variance as location weight are more accurately to forecast the rainfall data that has high variability and extreme point.

     

     


  • Keywords


    GSTAR-SUR, rainfall, ratio variance

  • References


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Article ID: 26393
 
DOI: 10.14419/ijet.v8i1.9.26393




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