红外与毫米波学报2025，Vol.44Issue(6)：865-874,10.DOI:10.11972/j.issn.1001-9014.2025.06.005
基于PSO-GA-SVM融合算法及荧光激光雷达遥测技术的生物气溶胶浓度预测
Prediction of bioaerosol concentration based on PSO-GA-SVM fusion algorithm and fluorescence lidar
摘要
Abstract
Bioaerosol particles spread widely in the air,and high concentrations of bioaerosols pose a great threat to hu-man health.To achieve early warning and prediction of atmospheric bioaerosol concentration,this paper uses fluores-cence lidar as the detection tool.Based on the acquisition of bioaerosol concentration profiles,combined with relevant parameters of the atmospheric environment,particle swarm optimization(PSO)and genetic algorithm(GA)are used to optimize the support vector machine(SVM)to establish a bioaerosol concentration profile prediction model.Using temperature,humidity,PM2.5,PM10,CO2,SO2,NO2,O3,wind speed and other related parameter data as inputs,and bioaerosol concentration profile data as outputs for model training,the prediction model parameter configuration is determined.New atmospheric environment parameters are reintroduced,and the trained model is used to predict the bio-aerosol concentration profile,which is compared with the bioaerosol concentration profile detected by fluorescence li-dar.At the same time,different algorithms are analyzed to optimize the model's predicted bioaerosol concentration and its relative error.关键词
荧光激光雷达/生物气溶胶/PSO-GA-SVM融合算法/浓度预测Key words
fluorescence lidar/bioaerosol/PSO-GA-SVM fusion algorithm/concentration prediction分类
天文与地球科学引用本文复制引用
饶志敏,李一成,李一秀,刘佳鑫,巩鑫,赵虎,毛建东..基于PSO-GA-SVM融合算法及荧光激光雷达遥测技术的生物气溶胶浓度预测[J].红外与毫米波学报,2025,44(6):865-874,10.基金项目
国家自然科学基金(42465007、42105140、42265009)、宁夏自然科学优秀青年基金(2022AAC05032)、北方民族大学研究生创新项目(YCX24345) Supported by the National Natural Science Foundation of China(42465007,42105140,42265009),the Natural Science Out-standing Youth Foundation of Ningxia Province(2022AAC05032),the Graduate Innovation Project of North Minzu University(YCX24345) （42465007、42105140、42265009）
