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融合时空特征的浓雾短临趋势预测算法

Algorithm of short-term prediction for dense fog′s trend based on fusion of spatio-temporal features
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摘要 为减少浓雾可能导致的各项损失,浓雾短临趋势预测已成为气象短临预测领域的研究热点。然而,目前相关研究较多侧重于浓雾所具有的时间特征,忽略其空间特征,从而导致预测准确率较低。为此,提出一种融合时空特征的浓雾短临趋势预测算法。该算法通过将多气象站点抽象为图数据的节点,引入图注意力网络,实现空间特征的提取;在此基础上,针对性地调整长短期记忆网络,结合时间信息,对所提取的空间特征进一步提取时间特征,实现特征级融合,并使用全连接层输出能见度预测值;最后根据能见度预测值,对浓雾短临趋势开展预测。本文所提算法针对美国国家环境信息中心的公开气象数据开展了2 h内的浓雾短临趋势预测实验,实验结果表明所提算法的F1-score和TS-score较基线模型有2%~12%的提升,证明所提算法具有良好的实际应用价值。 In order to reduce various losses that may be caused by dense fog,the short-term prediction of dense fog′s trend has become a research hotspot in the field of meteorological short-term prediction.However,current researches focus on the temporal features of dense fog while ignoring its spatial features,so the prediction accuracy is still at a relatively low level.To this end,this paper proposes an algorithm of short-term prediction for dense fog′s trend based on fusion of spatio-temporal features.The algorithm takes multi-stations as nodes in the graph data.By advancing Graph Attention Network,we realize the extraction of spatial features.On this basis,combined with time information,adjusting the Long-Short Term Memory network to further extract temporal features from the spatial features in order to realize feature-level fusion.Then we use the fully connected layer to output the predicted value of visibility.Further we can get to the predict result t based on the predicted value of visibility.We apply the algorithm on meteorological data released by National Centers for Environmental Information to carry out the experiment.The experimental results show that the F1-score and TS-score of the proposed algorithm take an improvement of 2%~12%on baseline models,which proves that the proposed algorithm has a great practical application value.
作者 应嘉顺 张金艺 陈琪 Ying Jiashun;Zhang Jinyi;Chen Qi(Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Shanghai University,Shanghai 200444,China;Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University,Shanghai 200444,China;Shanghai Sansi Institute for System Integration,Shanghai 201100,China)
出处 《电子测量技术》 北大核心 2023年第19期87-95,共9页 Electronic Measurement Technology
基金 上海市闵行区重大产业技术攻关计划(2022MH-ZD19) 十三五国家重点研发计划项目(2017YFB0403500) 高等学校学科创新引智计划(111)项目(D20031)资助
关键词 浓雾短临趋势预测 时空特征融合 图注意力网络 长短期记忆网络 short-term prediction for dense fog’s trend spatio-temporal features graph attention network long-short term memory
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