A Bayesian model for predicting monthly fire frequency in Kenya
Levi Orero,
Evans Otieno Omondi,
Bernard Oguna Omolo
Abstract:This study presents a comprehensive analysis of historical fire and climatic data to estimate the monthly frequency of vegetation fires in Kenya. This work introduces a statistical model that captures the behavior of fire count data, incorporating temporal explanatory factors and emphasizing the predictive significance of maximum temperature and rainfall. By employing Bayesian approaches, the paper integrates literature information, simulation studies, and real-world data to enhance model performance and gener… Show more
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