Predicting Forest Fire Using Remote Sensing Data And Machine Learning

TL;DR

This paper presents a machine learning approach using remote sensing data to predict forest fires in Indonesia, achieving high accuracy and offering a cost-effective alternative to traditional methods.

Contribution

A novel machine learning model utilizing remote sensing data for forest fire prediction, outperforming traditional systems and maintaining accuracy with reduced data.

Findings

Achieved over 0.81 ROC AUC in fire prediction

Outperformed baseline approaches with ROC AUC below 0.70

Maintained high performance with less data

Abstract

Over the last few decades, deforestation and climate change have caused increasing number of forest fires. In Southeast Asia, Indonesia has been the most affected country by tropical peatland forest fires. These fires have a significant impact on the climate resulting in extensive health, social and economic issues. Existing forest fire prediction systems, such as the Canadian Forest Fire Danger Rating System, are based on handcrafted features and require installation and maintenance of expensive instruments on the ground, which can be a challenge for developing countries such as Indonesia. We propose a novel, cost-effective, machine-learning based approach that uses remote sensing data to predict forest fires in Indonesia. Our prediction model achieves more than 0.81 area under the receiver operator characteristic (ROC) curve, performing significantly better than the baseline approach which never exceeds 0.70 area under ROC curve on the same tasks. Our model's performance remained above 0.81 area under ROC curve even when evaluated with reduced data. The results support our claim that machine-learning based approaches can lead to reliable and cost-effective forest fire prediction systems.