This article describes an analytical method for comparing geographical sites and transferring fog forecasting models, trained by Data Mining techniques on a fixed site, across Italian airports. This portability method uses a specific intersite similarity measure based on the Euclidean distance between the performance vectors associated with each airport site. Performance vectors are useful for characterizing geographical sites. The components of a performance vector are the performance metrics of an Ensemble descriptive model. In the tests carried out, the comparison method provided very promising results, and the forecast model, when applied and evaluated on a new compatible site, shows only a small decrease in performance. The portability schema provides a meta-learning methodology for applying predictive models to new sites where a new model cannot be trained from scratch owing to the class imbalance problem or the lack of data for a specific learning. The methodology offers a measure for clustering geographical sites and extending weather knowledge from one site to another.

中文翻译：

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雾事件预测数据挖掘模型的可移植性分析

本文介绍了一种用于比较地理位置和转移雾预报模型的分析方法，该模型通过数据挖掘技术在意大利机场的固定站点上进行训练。这种可移植性方法使用基于与每个机场站点相关的性能向量之间的欧几里德距离的特定站点间相似性度量。性能向量可用于表征地理位置。性能向量的组成部分是集成描述模型的性能指标。在进行的测试中，比较方法提供了非常有希望的结果，并且在新的兼容站点上应用和评估预测模型时，性能仅显示小幅下降。可移植性模式提供了一种元学习方法，用于将预测模型应用于由于类不平衡问题或缺乏特定学习数据而无法从头开始训练新模型的新站点。该方法提供了一种对地理站点进行聚类并将天气知识从一个站点扩展到另一个站点的措施。