Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction

TL;DR

This paper introduces a deep learning data augmentation method using conditional tabular GANs to improve wildfire risk prediction, aiding in wildfire prevention and reducing greenhouse gas emissions.

Contribution

The paper presents a novel application of conditional tabular GANs for wildfire risk prediction, outperforming baseline methods and demonstrating robustness across datasets.

Findings

Proposed GAN-based approach outperforms five baseline methods.

Method shows consistent robustness across different datasets.

Improved wildfire risk prediction can aid in GHG emission reduction.

Abstract

Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California. The high-level concentration of greenhouse gas (GHG) emitted by wildfires aggravates global warming that further increases the risk of more fires. Therefore, an accurate prediction of wildfire occurrence greatly helps in preventing large-scale and long-lasting wildfires and reducing the consequent GHG emissions. Various methods have been explored for wildfire risk prediction. However, the complex correlations among a lot of natural and human factors and wildfire ignition make the prediction task very challenging. In this paper, we develop a deep learning based data augmentation approach for wildfire risk prediction. We build a dataset consisting of diverse features responsible for fire ignition and utilize a conditional tabular generative adversarial network to explore the underlying patterns between the target value of risk levels and all involved features. For fair and comprehensive comparisons, we compare our proposed scheme with five other baseline methods where the former outperformed most of them. To corroborate the robustness, we have also tested the performance of our method with another dataset that also resulted in better efficiency. By adopting the proposed method, we can take preventive strategies of wildfire mitigation to reduce global GHG emissions.