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Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires

机译:机器学习方法和合成数据生成预测大型野火

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摘要

Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.
机译:野火在全球不同地区变得更频繁,以及预测何时何地发生的能力是复杂的过程。以高概率识别成为大型野火的野火事件是支持初始攻击计划的重要任务。在野火分析中使用不同的方法,包括基于物理学,统计和基于机器学习(ML)的方法。其中,基于机器学习的人是相对小说的。此外,由于野火的数量大于大野火的数量,所以要在ML模型中使用的数据集是不平衡的,导致结果过度或磨损结果。在本手稿中,我们建议从利益变量以及ML模型来生成合成数据,以预测大野火。具体地,已经评估了五种合成数据生成方法,并用四个ML方法分析它们的结果。当使用合成数据时,结果会产生预测功率的提高,在管理野火时提供了在决策支持系统(DSS)中被考虑的新方法。

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