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A Bayesian model for predicting monthly fire frequency in Kenya.

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This study introduces a Bayesian Negative Binomial model to forecast monthly vegetation fires in Kenya using temperature and rainfall data. The new model offers superior accuracy and prediction intervals compared to traditional methods for fire management.

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Area of Science:

  • Environmental Science
  • Climate Science
  • Statistical Modeling

Background:

  • Vegetation fires pose significant risks in Kenya, necessitating accurate forecasting for effective management.
  • Historical fire and climatic data are crucial for understanding fire regimes and developing predictive models.

Purpose of the Study:

  • To develop and evaluate a statistical model for estimating and forecasting the monthly frequency of vegetation fires in Kenya.
  • To compare the performance of a proposed Bayesian Negative Binomial (BNB) model against the traditional Negative Binomial (NB) model.

Main Methods:

  • Utilized historical fire data and climatic variables (maximum temperature, rainfall) from 2000-2018 for Kenya.
  • Employed Bayesian approaches to integrate prior information, simulation studies, and real-world data for model enhancement.
  • Applied the Negative Binomial (NB) and Bayesian Negative Binomial (BNB) models to forecast monthly fire occurrences.

Main Results:

  • The Bayesian Negative Binomial (BNB) model demonstrated superior performance over the Negative Binomial (NB) model in simulations and real-world data analysis.
  • BNB model showed lower Root Mean Square Error (RMSE) and Mean Absolute Scaled Error (MASE), and reduced bias compared to the NB model.
  • The BNB model provided more precise prediction intervals that closely aligned with actual fire counts, indicating better forecasting capability.

Conclusions:

  • The Bayesian Negative Binomial model is a more effective tool for forecasting monthly vegetation fire frequency in Kenya.
  • Accurate fire prediction, informed by climatic factors and advanced statistical techniques, is vital for developing proactive fire control strategies.
  • This research contributes to a better understanding of fire regimes and supports mitigation efforts in Kenya.