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Modelling of fire count data: fire disaster risk in Ghana
Caleb Boadi1, Simon K Harvey1, Agyapomaa Gyeke-Dako1
1Department of Finance, University of Ghana Business School, Accra, Ghana.
Springerplus
|December 25, 2015
Summary
This study models fire risks in Ghana using statistical distributions, identifying the Negative Binomial Distribution as best for fire frequency and fatality data. This informs an early warning system to mitigate societal fire threats.
Area of Science:
- Ecological modeling
- Statistical analysis
- Risk assessment
Background:
- Ecological count data requires robust distribution fitting for accurate modeling.
- Societal fire risks necessitate effective early warning systems and spatial risk assessments.
- Understanding fire dynamics is crucial for communities, risk managers, and governments.
Purpose of the Study:
- To develop a fire-prediction model and spatial graph for observed fire count data.
- To identify the most suitable statistical distribution for modeling fire frequency and fatality.
- To provide a regional assessment of fire risk in Ghana.
Main Methods:
- Fitting empirical probability distribution models to fire count data.
- Comparing empirical distributions with theoretical stochastic process distributions.
- Utilizing Negative Binomial Distribution for fire frequency and loss data.
Main Results:
- The Negative Binomial Distribution effectively models fire frequency and fatality count data in Ghana.
- A spatial map was created for observed fire frequency and fatality from 2007-2011.
- The study provides a foundational regional assessment of fire risk.
Conclusions:
- The Negative Binomial Distribution is recommended for modeling fire count data in Ghana.
- The developed fire-prediction model and spatial graph can enhance early warning systems.
- Informed decision-making is facilitated for mitigating fire threats.
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