摘要
重污染天气是“十四五”时期大气污染治理的重点工作,在重污染天气时期对风险源进行精准识别,可以及时发出预警,做好环境污染治理,防止污染事件进一步加重.基于网格化监测技术获取的数据,本文提出一种结合残差网络(ResNet)、图卷积网络(GCN)和门控循环网络(GRU)的深度学习模型ResGCN-GRU,该模型主要应用于重污染天气时期识别风险源.重污染天气的风险源往往都是区域性的,具有明显的时空特征,因而本文先利用GCN网络提取监测点位之间的空间特征,同时利用ResNet解决多层GCN带来的过平滑以及梯度消失问题;再利用GRU提取风险源的时间特征,最后将全连接层融合的时空特征输入到Softmax激活函数得到二分类概率值,再根据概率值得到分类结果.为验证本文提出的模型性能,本文基于沈阳市72个监测点位的数据,通过精确度、召回率以及综合评价指标对GCN、LSTM、GRU和GCN-GRU进行对比,实验结果表明ResGCN-GRU模型分类效果的精确度分别要好16.9%、4.3%、3.1%、2.9%,证明了本文提出的模型在大气风险源识别方面更加有效,可以根据风险源数据的时空特征达到对风险源的精准识别.
Heavy pollution weather is the key target of air pollution control during the 14th Five-Year Plan period.Accurate identification of risk sources during the period of heavy pollution weather paves the way for early warning in time,effective environmental pollution control,and prevention of the further aggravation of pollution incidents.On the basis of the data obtained by grid monitoring technology,this study proposes a deep learning model combining the residual network(ResNet),graph convolutional network(GCN),and gated recurrent unit(GRU)network,i.e.,the ResGCN-GRU.This model is mainly used to identify risk sources during the period of heavy pollution weather.The risk sources of such weather are often regional and have salient spatiotemporal features.Therefore,this study starts by extracting the spatial features among monitoring points with the GCN and solving the problems of over-smoothing and gradient disappearance caused by the multi-layer GCN with the ResNet.Then,the GRU is used to extract the temporal features of risk sources.Finally,the spatiotemporal features fused by the fully connected layer are input into the Softmax activation function to obtain the binary classification probability,which is further used to obtain the classification result.To verify the performance of the proposed model,this study analyzes the data of 72 monitoring points in Shenyang and compares GCN,long short-term memory(LSTM),GRU,and GCN-GRU in accuracy,recall rate,and comprehensive evaluation indicators.The experimental results show that the classification accuracy of the ResGCN-GRU model is 16.9%,4.3%,3.1%,and 2.9%higher than that of the above four models,respectively,which proves that the model proposed in this study is more effective in identifying air risk sources,and it can accurately identify risk sources according to the spatiotemporal features of risk source data.
作者
祁柏林
赵娅倩
魏建勋
刘首正
QI Bo-Lin;ZHAO Ya-Qian;WEI Jian-Xun;LIU Shou-Zheng(Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;University of Chinese Academy of Sciences,Beijing 100049,China;Liaoning Fuxin Ecological Environment Monitoring Center,Fuxin 123000,China;Liaoning Environmental Monitoring Association,Shenyang 110161,China)
出处
《计算机系统应用》
2023年第6期301-307,共7页
Computer Systems & Applications
基金
辽宁省中央引导地方科技发展专项(2021010211-JH6/105)
沈阳市中青年科技创新人才支持计划(RC210360)