基于生成对抗网络的野火预测减少温室气体排放
Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction
摘要：
在过去的十年中，野火的数量在世界各地显著增加，特别是在加利福尼亚州。野火排放的高浓度温室气体（GHG）加剧了全球变暖，进一步增加了更多火灾的风险。因此，准确预测野火的发生，对于预防大规模、长时间的野火，减少温室气体排放，有着重要的意义。已经探索了各种方法来预测野火风险。然而，许多自然和人为因素与野火发生之间的复杂相关性使得预测任务非常具有挑战性。在本文中，我们开发了一种基于深度学习的数据增强方法用于野火风险预测。我们构建了一个由负责火灾点火的不同特征组成的数据集，并利用条件表格生成对抗网络来探索风险水平目标值与所有相关特征之间的潜在模式。为了公平和全面的比较，我们将我们提出的方案与其他五种基线方法进行比较，前者优于大多数方法。为了证实鲁棒性，我们还使用另一个数据集测试了我们方法的性能，该数据集也具有更好的效率。通过采用所提出的方法，我们可以采取预防性的野火缓解策略，以减少全球温室气体排放。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
