Application of artificial neural network forecasts to predict fog at Canberra International Airport
Application of artificial neural network forecasts to predict fog at Canberra International Airport
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DOI：
10.1175/waf980.1
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发表时间：
2007-04-01
影响因子：
2.9
通讯作者：
Lellyett, Stephen
中科院分区：
文献类型：
--
作者：
Fabbian, Dustin;de Dear, Richard;Lellyett, Stephen
雾的发生会对航空运输运行产生重大影响，对航空安全起着重要作用。1993年，仅悉尼机场航空预报的经济价值估计为澳航680万美元(澳元)。尽管数值天气预报指南和雾现象模型有所改进，但雾的预测仍然很困难。本文评估了人工神经网络(ANN)对堪培拉国际机场(YSCB)这类事件提供准确预报的能力。与传统的统计技术不同，人工神经网络非常适合于涉及复杂的非线性相互作用的问题，因此在雾预测中具有潜在的应用前景。从澳大利亚气象局获得的44年标准气象观测数据库被用来开发、训练、测试和验证旨在预测雾发生的神经网络。雾预报辅助工具是从当地标准时间0600开始开发的，适用于3、6、12和18小时的提前时间。通过对相关工作特性曲线的分析，评估了不同ANN结构的预测能力。结果表明，人工神经网络在所有四个提前期都能提供良好的识别能力。结果对不同输入参数的误差摄动具有较强的鲁棒性。建议在编制YSCB预报时纳入这类模式，并在其应用中推广这项技术，以涵盖其他类似易起雾的航空地点。
The occurrence of fog can significantly impact air transport operations, and plays an important role in aviation safety. The economic value of aviation forecasts for Sydney Airport alone in 1993 was estimated at $6.8 million ( Australian dollars) for Quantas Airways. The prediction of fog remains difficult despite improvements in numerical weather prediction guidance and models of the fog phenomenon. This paper assesses the ability of artificial neural networks ( ANNs) to provide accurate forecasts of such events at Canberra International Airport ( YSCB). Unlike conventional statistical techniques, ANNs are well suited to problems involving complex nonlinear interactions and therefore have potential in application to fog prediction. A 44-yr database of standard meteorological observations obtained from the Australian Bureau of Meteorology was used to develop, train, test, and validate ANNs designed to predict fog occurrence. Fog forecasting aids were developed for 3-, 6-, 12-, and 18-h lead times from 0600 local standard time. The forecasting skill of various ANN architectures was assessed through analysis of relative operating characteristic curves. Results indicate that ANNs are able to offer good discrimination ability at all four lead times. The results were robust to error perturbation for various input parameters. It is recommended that such models be included when preparing forecasts for YSCB, and that the technique should be extended in its application to cover other similarly fog-prone aviation locations.