| 基于BP神经网络模型的雷电潜势预报 |
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| 引用本文： | 陈勇伟,郑涛,王汉堃,王琦,梁耀丹.基于BP神经网络模型的雷电潜势预报[J].甘肃气象,2013(3):595-601. |
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| 作者姓名： | 陈勇伟 郑涛 王汉堃 王琦 梁耀丹 |
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| 作者单位： | [1]甘肃省防雷中心,甘肃兰州730020 [2]内蒙古雷电预警防护中心,内蒙古呼和浩特010051 [3]广西贵港市气象局,广西贵港573100 |
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| 摘 要： | 为了使用神经网络较好地解决在雷电潜势预报中常见的非线性问题，本文通过计算南京地区2008年6～8月46个对流参数与雷电发生的相关系数，选取了与雷电发生关系较好的刀、SI、CIN等7个对流参数作为BP神经网络的输入因子。利用2008年的资料所建立的BP神经网络模型，预报了南京地区2009年6～8月的雷暴活动潜势，结合实际雷暴发生情况，得到此模型的POD为80．9％，FAR为9．5％，CSI为74．5％，PDFD为2．9％，FOM为19．1％。表明该BP模型预报准确率较高，性能稳定，有较好的推广价值。
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| 关 键 词： | 对流参数 因子组合 雷电潜势预报 BP神经网络 |
Thunderstorm Potential Prediction Based on Back Propagation Neural Network |
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| CHEN Yongwei,ZHENG Tao,WANG Hankun,WANG Qi,LIANG Yaodan.Thunderstorm Potential Prediction Based on Back Propagation Neural Network[J].Gansu Meteorology,2013(3):595-601. |
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| Authors: | CHEN Yongwei ZHENG Tao WANG Hankun WANG Qi LIANG Yaodan |
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| Institution: | 1. Lightning Protection Center of Gansu Province, Lanzhou 730020, China ; 2. Inner Mongolia Linhtning Early Warning Protection Center, Huhehaote 010051, China; 3. Guigang Meteorological Bureau of Guangxi, Guigang 537100, China) |
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| Abstract: | In order to use neural networks to solve common nonlinear problem in lightning potential trend prediction, the correlation co- efficients were calculated between forty -six convective parameters and thunderstorms occurring from June to August in 2008 in Nan- ring. In these convective parameters, seven convective factors among them had better relationship with thunderstorms occurrence, in- cluding TF, SI, SWEAT, Tlfc, CIN, DCI and PW indexes, then these seven convective parameters were selected as the input factors of the neural network model which contained seven input layers, twelve hidden layers and one output layer. On the basis of back propa- gation neural network model built by the data of 2008, the thunderstorm potential trend from June to August in 2009 in Nanjing were predicted including the thunderstorm days and non - thunderstorm days. According to the score standard, the POD, FAR, CSI, PDFD and F0M of the model were 74.5% , 9.5% , 74.5% 2.9% and 19.1% , respectively, which indicated that this back propagation neu- ral network model had better forecast accuracy and its performance was steady, it can be well applied in thunderstorm potential trend prediction. |
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| Keywords: | convective parameters factor combination thunderstorm potential trend prediction back propagation neural network model |
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