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.
翻译:在过去几十年中,森林砍伐和气候变化造成越来越多的森林火灾。在东南亚,印度尼西亚是热带泥炭地森林火灾影响最大的国家。这些火灾对气候产生了重大影响,造成了广泛的健康、社会和经济问题。现有的森林火灾预测系统,如加拿大森林火灾危险评级系统,以手工制作的特征为基础,需要在实地安装和维护昂贵的仪器,这对印度尼西亚等发展中国家来说可能是一个挑战。我们提出了一种新型的、成本效益高的、基于机器学习的方法,利用遥感数据预测印度尼西亚的森林火灾。我们的预测模型在接收器操作员特点(ROC)曲线下实现了0.81个以上的区域,比在同一任务中从未超过ROC曲线下0.70个区域的基线方法要好得多。我们的模型的性能仍然超过0.81个区域,即使在用较少的数据进行评估时,在ROC曲线下仍然处于0.81个区域。结果支持我们的说法,即基于机器学习的方法可以导致可靠和成本效益高的森林火灾预测系统。