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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain.

María Bugallo¹, María Dolores Esteban¹, Manuel Francisco Marey-Pérez²

¹Universidad Miguel Hernández de Elche, Centro de Investigación Operativa, Spain.

Journal of Environmental Management

|December 16, 2022

Summary

Accurate wildfire prediction models are essential for resource allocation. This study uses zero-inflated negative binomial mixed models to predict wildfire occurrences in Spain, accounting for seasonal variations and non-occurrence.

Keywords:

Bootstrap Mean squared error Prediction Wildfire forecasting Zero-inflated negative binomial mixed model

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Area of Science:

Ecology
Environmental Science
Statistical Modeling

Background:

Wildfires are increasing globally, necessitating better predictive tools.
Mediterranean regions experience high wildfire frequency, often concentrated seasonally.
Predicting wildfire numbers requires models that handle both occurrence and non-occurrence, especially with seasonal variations.

Purpose of the Study:

To develop and apply accurate country-scale predictive models for wildfires.
To utilize statistical modeling for organizing firefighting resources effectively.
To analyze and predict wildfire occurrences in Spain by province and month.

Main Methods:

Zero-inflated negative binomial mixed models were employed to analyze wildfire data.
A parametric bootstrap method was used for error estimation and prediction intervals.
The methodology was applied to Spanish wildfire data from 2002-2015.

Main Results:

The models successfully captured patterns of wildfire occurrence and non-occurrence.
Predictions were generated for wildfire numbers across Spanish provinces and months.
The study provides a robust statistical framework for wildfire prediction.

Conclusions:

Zero-inflated negative binomial mixed models are suitable for predicting seasonal wildfire data.
Accurate wildfire prediction is crucial for effective resource management and firefighting strategies.
The developed statistical methodology and software can be applied to other regions facing similar wildfire challenges.