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Guidelines for effective evaluation and comparison of wildland fire occurrence prediction models
International Journal of Wildland Fire ( IF 3.1 ) Pub Date : 2021-01-29 , DOI: 10.1071/wf20134
Nathan Phelps , Douglas G. Woolford

Daily, fine-scale spatially explicit wildland fire occurrence prediction (FOP) models can inform fire management decisions. Many different data-driven modelling methods have been used for FOP. Several studies use multiple modelling methods to develop a set of candidate models for the same region, which are then compared against one another to choose a final model. We demonstrate that the methodologies often used for evaluating and comparing FOP models may lead to selecting a model that is ineffective for operational use. With an emphasis on spatially and temporally explicit FOP modelling for daily fire management operations, we outline and discuss several guidelines for evaluating and comparing data-driven FOP models, including choosing a testing dataset, choosing metrics for model evaluation, using temporal and spatial visualisations to assess model performance, recognising the variability in performance metrics, and collaborating with end users to ensure models meet their operational needs. A case study for human-caused FOP in a provincial fire control zone in the Lac La Biche region of Alberta, Canada, using data from 1996 to 2016 demonstrates the importance of following the suggested guidelines. Our findings indicate that many machine learning FOP models in the historical literature are not well suited for fire management operations.



中文翻译:

有效评估和比较荒地火灾发生预测模型的指南

每日,精细的空间显式野外火灾发生预测(FOP)模型可以为火灾管理决策提供依据。许多不同的数据驱动建模方法已用于FOP。一些研究使用多种建模方法来为同一区域开发一组候选模型,然后将它们相互比较以选择最终模型。我们证明了经常用于评估和比较FOP模型的方法可能会导致选择对运营使用无效的模型。我们着重于日常消防管理操作的时空显式FOP建模,概述并讨论了一些评估和比较数据驱动FOP模型的准则,包括选择测试数据集,选择模型评估指标,使用时空可视化评估模型性能,识别性能指标的可变性,并与最终用户合作以确保模型满足其运营需求。使用加拿大1996年至2016年的数据对加拿大艾伯塔省Lac La Biche地区一个省级消防区中人为造成的FOP进行的案例研究表明,遵循建议的准则非常重要。我们的发现表明,历史文献中的许多机器学习FOP模型都不太适合进行消防管理。使用1996年至2016年的数据证明了遵循建议指南的重要性。我们的发现表明,历史文献中的许多机器学习FOP模型都不太适合进行消防管理。使用1996年至2016年的数据证明了遵循建议指南的重要性。我们的发现表明,历史文献中的许多机器学习FOP模型都不太适合进行消防管理。

更新日期:2021-02-01
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