基于神经网络的单站雾预报试验

采集大连某机场2004—2007年大雾、轻雾和无雾天气事件共186例,选取雾天气事件前期(前一日08:00,14:00,20:00(北京时)实测资料)的温、压、湿、风等要素指标为预报因子,基于学习向量量化神经网络(learning vector quantization,LVQ),采用逐级预报思想建立起某机场雾天气事件的预报模型。在网络训练过程中,动态调整网络神经元比例参数,提高模型的预报能力;采用根据检验准确率适时终止训练的"先停止"技术,有效提高了模型的泛化能力。预报试验表明:无论是拟合率还是独立预报准确率,模型均已达到较高水准,具有实际应用意义。

Fog Forecast Experiment of Single Station Based on LVQ Neural Network

The generating and dissolving of fogs are too complex for empirical and linear systems methods to forecast and these methods cannot meet the needs of flight training.To meet this end,a new fog predicting model is proposed based on learning vector quantization neural network.The forecasting model of fog weather events is established using sequential forecast idea,adopting principal component analysis(PCA) and learning vector quantization network too.186 cases of heavy fog,mist or fog-free weather events on a...