| 大气PM2.5与健康:从暴露、危害到干预的系统研究进展 |
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| 引用本文： | 李晓理, 张山, 王康. 基于图像质量分析的PM2.5空气质量预测[J]. 北京工业大学学报, 2020, 46(2): 191-198. DOI: 10.11936/bjutxb2018110028 |
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| 作者姓名： | 李晓理 张山 王康 |
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| 作者单位： | 1.北京工业大学信息学部, 北京 100124;2.计算智能与智能系统北京市重点实验室, 北京 100124;3.数字社区教育部工程研究中心, 北京 100124;4.北京未来网络科技高精尖创新中心, 北京 100124 |
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| 摘 要： | 为了提高空气污染物PM2.5质量浓度预测的准确性，提出了一种基于图像数据预测PM2.5质量浓度的方法.首先用手机或相机获取图像数据，然后用图像质量分析模型提取与PM2.5质量浓度相关的特征向量作为输入，建立一个基于粒子群优化（particle swarm optimization，PSO）算法的支持向量回归机（support vector regression，SVR）（PSO-SVR）预测模型来估计PM2.5的质量浓度.实验结果表明，与SVR模型和用遗传算法（genetic algorithm，GA）优化的支持向量回归机（GA-SVR）模型相比，PSO-SVR模型在预测准确性和实施效率方面具有更好的预测性能.
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| 关 键 词： | PM2.5质量浓度 支持向量回归机 粒子群优化算法 特征提取 图像质量评价 |
| 收稿时间： | 2018-11-25 |
Atmospheric PM2.5 and health:progress in the study of exposure,hazards, and interventions |
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| LI Xiaoli, ZHANG Shan, WANG Kang. PM2.5 Air Quality Prediction Based on Image Quality Analysis[J]. Journal of Beijing University of Technology, 2020, 46(2): 191-198. DOI: 10.11936/bjutxb2018110028 |
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| Authors: | LI Xiaoli ZHANG Shan WANG Kang |
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| Affiliation: | 1.Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;2.Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;3.Engineering Research Center of Digital Community, Beijing 100124, China;4.Beijing Advanced Innovation Center for Future Internet Technology, Beijing 100124, China |
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| Abstract: | To improve the prediction accuracy of air pollutants of the mass concentration of PM2.5, a method of PM2.5 mass concentration prediction based on collected image data were proposed. First, image data were acquired by mobile phones or cameras, and then feature vectors related to PM2.5 mass concentration were extracted by image quality analysis model as input. A support vector regression (SVR) prediction model based on particle swarm optimization (PSO) algorithm (PSO-SVR) was established to estimate the mass concentration of PM2.5. Results show that the prediction accuracy and efficiency of the PSO-SVR model are better than that of the SVR model and the support vector regression model optimized by genetic algorithm (GA-SVR). |
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| Keywords: | PM2.5 mass concentration support vector regression (SVR) particle swarm optimization (PSO) algorithm feature extraction image quality assessment |
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