摘要
为了提高空气污染物PM质量浓度预测的准确性,提出了一种基于图像数据预测PM质量浓度的方法.2.52.5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PM质量浓度相关的特征向量作为输入,建立2.5一个基于粒子群优化(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PM的质量浓度.实验结果表明,与SVR模型和用遗传算法(genetic algorithm,GA)2.5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.
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).
作者
李晓理
张山
王康
LI Xiaoli;ZHANG Shan;WANG Kang(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China;Engineering Research Center of Digital Community,Beijing 100124,China;Beijing Advanced Innovation Center for Future Internet Technology,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2020年第2期191-198,共8页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61873006)
国家重点研发计划资助项目(2018YFC1602704,2018YFB1702704)
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
PM2.5 mass concentration
support vector regression(SVR)
particle swarm optimization(PSO)algorithm
feature extraction
image quality assessment
