摘要
对空气质量影响因素进行分析与预测是确定关键因素和追溯污染源的简单而有效的途径。针对当前空气质量预测方法预测精度不高,且极易陷入局部最优值的问题,本文提出了一种基于交叉递归定量分析(CRQA)与深度置信网络-极限学习机(DBN-ELM)的空气质量数据预测方法。首先,采用CRQA对多种空气质量影响因素间的关联度进行分析,筛选出影响空气质量的重要因素。然后,将获取的空气质量主要影响因素输入到DBN-ELM模型中进行预测。其中,DBN用于提取空气质量主要因素的关键特征,ELM用于最终空气质量时序数据的非线性逼近。实验结果表明,在北京奥体中心站点空气质量数据集上,该模型的RMSE值为1.775 9,R~2为0.983 3,优于其他对比模型。进一步,采用散点图与分位比较图方法验证了所提出模型的有效性。
It is a simple and effective way to determine the key factors and trace the causes of pollution by analyzing and predicting the influencing factors of air quality. Aiming at the prediction accuracy of current air quality prediction methods is not high, and it is easy to fall into the local optimal value problem, a novel model is proposed, which is based on Cross Recurrence Quantification Analysis(CRQA) and Deep Belief Network-Extreme Learning Machine(DBN-ELM) air quality data prediction method. Firstly, CRQA is used to analyze the correlation degree among various factors affecting air quality and screen out the critical factors affecting air quality. Then, the main influencing factors of air quality obtained are input into the DBN-ELM model for prediction. Concretely, DBN is used to extract key features of main air quality factors, and ELM is used for nonlinear approximation of final air quality time series data. The experimental results show that in the air quality data set of the Beijing Olympic Sports Center, the RMSE value and R~2 value of this model are 1.7759 and 0.9833 respectively, which are better than other models. Furthermore, the effectiveness of the proposed model is verified by scatterplot and quantile-quantile plot.
作者
李志刚
秦林林
付多民
孙晓川
Li Zhigang;Qin Linlin;Fu Duomin;Sun Xiaochuan(College of Artificial Intelligence,North China University of Science and Technology,Tangshan 063210,China;Hebei Key Laboratory of Industrial Intelligent Perception,Tangshan 063210,China)
出处
《电子测量技术》
北大核心
2022年第19期76-82,共7页
Electronic Measurement Technology
基金
河北省高等学校科学技术研究项目(ZD2021088)
国家重点研发计划项目(2017FE0135700)资助。
