RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD

Abstract

Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.