PENERAPAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA LAPISAN OZON SEBAGAI EARLY WARNING BENCANA DI INDONESIA - Defi Yusti Faidah (PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL), 2016)
The ozone layer depleting led to an increased ultraviolet radiation that falls to the earth's surface. This can have a negative impact on the environment and health. Effects caused by the depletion of the ozone layer, among others, environmental degradation, limited water resources, damage to the marine food chain, the destruction of coral reefs and other marine resources, declining agricultural production that could undermine food security, and other natural disasters. It takes a statistical model to predict the occurrence of rain in the city of Bandung which is intended as an early warning to disasters disebablan by the depletion of the ozone layer. The statistical model used is Geographically Weighted Regression (GWR). Modelling GWR attention to the geographical location of the region because each region has different characteristics. In this study involving variables expected effect on the ozone layer in Indonesia. Variables used are CO2 and water vapor. Based on modeling results can be known factors that significantly affect the ozone layer for each location point. Retrieved information is interesting and important differences from each point the location of the factors that significantly influence the ozone layer. Expected by unknown factors that influence can be used as input to the government to minimize the occurrence of the disaster caused by the depletion of the ozone layer.English version of the abstract be written here. Keywords: Early Warning, GWR, Ozon.