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Related Experiment Video

Updated: Nov 3, 2025

Wind Tunnel Experiments to Study Chaparral Crown Fires

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Published on: November 14, 2017

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires.

Fernando-Juan Pérez-Porras¹, Paula Triviño-Tarradas¹, Carmen Cima-Rodríguez²

¹Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Córdoba, 14071 Córdoba, Spain.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This summary is machine-generated.

This study introduces synthetic data generation to improve machine learning models for predicting large wildfires. Using synthetic data enhances prediction accuracy, aiding wildfire management and initial attack planning.

Keywords:

burned area imbalanced data logistic regression multi-layer perceptron prediction large wildfire

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Area of Science:

Environmental Science
Computer Science
Data Science

Background:

Wildfires are increasing globally, necessitating accurate prediction for effective management.
Predicting large wildfire events is crucial for initial attack planning and resource allocation.
Machine learning (ML) models offer novel approaches to wildfire analysis but face challenges with imbalanced datasets.

Purpose of the Study:

To address the challenge of imbalanced datasets in machine learning for wildfire prediction.
To evaluate the effectiveness of synthetic data generation methods in improving large wildfire prediction.
To enhance the accuracy of wildfire prediction models for better decision support.

Main Methods:

Generation of synthetic data using five distinct methods.
Evaluation of synthetic data performance with four machine learning models.
Analysis of prediction power improvements using generated synthetic data.

Main Results:

Synthetic data generation significantly improved the prediction power of machine learning models.
The integration of synthetic data offers a viable solution to dataset imbalance issues.
Enhanced prediction capabilities were observed across evaluated machine learning methods.

Conclusions:

Synthetic data generation is a promising technique for improving large wildfire prediction models.
This approach can enhance the reliability of Decision Support Systems (DSS) for wildfire management.
The study provides a novel method to improve the accuracy and effectiveness of wildfire forecasting.