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Gradient boosting with extreme-value theory for wildfire prediction.

Jonathan Koh^1,2

¹Institute of Mathematics, EPFL, Lausanne, Switzerland.

|April 24, 2023

Summary

This summary is machine-generated.

This study presents a machine learning approach for wildfire prediction using extreme value theory, achieving second place in a data challenge. The methods improved prediction accuracy for wildfire counts and sizes across the contiguous US.

Keywords:

Cross-validation Generalized Pareto distribution Gradient boosting Loss likelihood Machine learning Wildfire prediction

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Area of Science:

Environmental Science
Data Science
Machine Learning

Background:

Wildfire prediction is crucial for risk management.
Accurate modeling of extreme events like wildfires is challenging.
Existing methods may not fully capture spatial dependencies or extreme value characteristics.

Purpose of the Study:

To detail the Kohrrelation team's approach for the 2021 Extreme Value Analysis data challenge.
To predict wildfire counts and sizes in the contiguous US.
To benchmark a novel machine learning approach against existing methods.

Main Methods:

Applied extreme value theory within a machine learning framework.
Utilized theoretically justified loss functions for gradient boosting.
Developed and implemented a spatial cross-validation scheme.

Main Results:

The spatial cross-validation scheme proved superior to naive cross-validation for performance estimation.
The proposed approach demonstrated competitive performance against other boosting methods.
The team secured second place in the competition ranking.

Conclusions:

The integration of extreme value theory with gradient boosting offers a robust method for wildfire prediction.
Spatial cross-validation is an effective technique for evaluating predictive models in geographical contexts.
The approach shows significant promise for improving wildfire risk assessment and management.