Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

中文翻译：

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北加州以数据为依据的野火风险预测

多年来，野火肆虐，困扰着加利福尼亚州，造成了经济和环境损失。2018年，山火造成了近8亿美元的经济损失，并在加利福尼亚州夺走了100多条生命。超过160万英亩的土地被烧毁，对环境造成了巨大破坏。尽管最近，研究人员在预测野火风险方面引入了机器学习模型和算法，但这些结果集中在特殊的角度，并且仅限于有限的数据参数。在本文中，我们提出了两种基于随机森林模型的数据驱动的机器学习方法，以预测蒙蒂塞洛和加利福尼亚州温特斯附近地区的野火风险。这项研究演示了如何开发模型并将其与综合数据参数（如电力线，地形，以及不同角度的植被，从而提高了在预测包括火灾在内的野火风险时的时空准确性。组合模型使用空间和时间参数作为单个组合数据集来训练和预测火灾风险，而集成模型则被提供了单独的参数，这些参数随后被堆叠以用作单个模型。我们的实验表明，在准确性方面，与随机森林模型在单独的空间数据上的集成相比，组合模型产生了更好的结果。使用接收器工作特征（ROC）曲线，学习曲线和评估指标（例如：准确性，混淆矩阵和分类报告）验证了模型。研究结果表明，在预测野火风险方面，最先进的准确性达到了92％，