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Toward a more resilient Thailand: Developing a machine learning-powered forest fire warning system.

Jing Tang^1,2, Manapat Weeramongkolkul¹, Supanida Suwankesawong³

¹International School of Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai, Pathumwan, Bangkok, 10330, Thailand.

|July 29, 2024

Summary

This summary is machine-generated.

This study developed an accurate machine learning model for forest fire detection in Thailand using satellite data and gas measurements. The XGBoost model achieved 99.6% accuracy, offering a faster, cost-effective early warning system.

Keywords:

Forest fire Machine learning Thailand Warning system

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Area of Science:

Environmental Science
Computer Science
Remote Sensing

Background:

Forest fires pose a significant threat in Thailand, with current detection methods being inefficient.
There is a critical need for an effective forest fire warning system to mitigate damage.

Purpose of the Study:

To develop a binary machine learning classifier for early forest fire detection in Thailand.
To evaluate various classification models using satellite-derived gas data.

Main Methods:

Utilized satellite data from Google Earth Engine (January 2019-October 2022).
Incorporated four gas variables: carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone.
Compared linear classifiers, gradient-boosting classifiers, and artificial neural networks, with a focus on XGBoost.

Main Results:

The XGBoost model demonstrated superior performance with 99.6% accuracy and a 0.939 ROC-AUC score.
Decision-tree-based algorithms, like XGBoost, proved highly effective for forest fire prediction.

Conclusions:

An integrated forest fire warning system combining gas sensors and geospatial data is essential.
Future research should prioritize decision-tree algorithms and incorporate feedback mechanisms for continuous model improvement.