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首页> 外文会议>IEEE International Workshop on Metrology for Aerospace >Short range fog forecasting by applying data mining techniques: Three different temporal resolution models for fog nowcasting on CDG airport
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Short range fog forecasting by applying data mining techniques: Three different temporal resolution models for fog nowcasting on CDG airport

机译：通过应用数据挖掘技术进行短程雾预报：CDG机场近距离预报的三种不同时间分辨率模型

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摘要

Forecasting fog is an important issue for air traffic safety because adverse visibility conditions represent one of the major causes of traffic delay and of the economic loss associated with such phenomena. In such context the present work illustrates a Data Mining application for the fog forecast on a short time range (1 hour, 2 hours and 3 hours) on Paris Charles de Gaulle airport. Indeed three predictive models have been built using an historical dataset of 17 years of fog observations and other relevant meteorological parameters collected in the SYNOP message and by applying a BayesNet algorithm. The performances evaluation show that the best model for the fog forecast is that on one hour time range, presenting a percentage of correct classified instances of 97% and a true positive rate of 88%. The other implemented models show slightly worse performances with a percentage of correct classified instances of about 96% and 95% respectively and true positive rates of 80% and 71%. The work has been carried on according to the standard process (CRISP-DM) for Knowledge Discovery in Meteorological Database Process.
机译：预测雾气是航空交通安全的重要问题，因为不利的能见度条件是造成交通延误和与此类现象相关的经济损失的主要原因之一。在这种情况下，本工作说明了在巴黎戴高乐机场的短时间范围（1小时，2小时和3小时）内进行雾预报的数据挖掘应用程序。实际上，已经使用17年雾观测的历史数据集以及在SYNOP消息中收集的其他相关气象参数，并通过应用BayesNet算法，建立了三个预测模型。性能评估表明，雾预报的最佳模型是在一小时的时间范围内，呈现正确分类实例的百分比为97％，真实阳性率为88％。其他实施模型显示的性能稍差，正确分类实例的百分比分别约为96％和95％，真实阳性率分别为80％和71％。该工作已按照用于气象数据库过程中的知识发现的标准过程（CRISP-DM）进行。

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