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| 基于BP神经网络的儿童卧室内灰尘PAEs浓度预测 |
| Prediction of PAEs Concentration in Dust in Children’s Bedroom Based on BP Neural Network |
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| DOI： |
| 中文关键词: 邻苯二甲酸酯 反向传播神经网络 浓度预测 灰尘 儿童卧室 |
| 英文关键词: Phthalate Back propagation neural network Concentration prediction Dust Childrens bedroom |
| 基金项目:国家自然科学基金资助项目（51708347，81861138005）；国家重点研发计划基金资助项目〖JP2〗（2017YFC0702700）；上海市自然科学基金资助项目（21ZR1444800） |
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| 中文摘要: |
| 在实测数据的基础上，以邻苯二甲酸酯（PAEs）的各类影响因素为自变量，PAEs浓度为因变量，采用Back-propagation(BP)神经网络建立儿童卧室内PAEs浓度预测模型。结果表明，该模型的预测效果较理想，其中，STD比值均＞0.5，NMB均接近0，EMR均＜19%。以室内环境与儿童健康（CCHH）课题组天津地区的相关数据，对DEHP浓度进行预测，其实测值与预测值平均值的EMR为7.7%，表明该模型预测精度较高。 |
| 英文摘要: |
| Based on measured data, the prediction model of PAEs concentration in childrens bedroom was established by back propagation (BP) neural network with various influencing factors of phthalates (PAEs) as independent variables and PAEs concentration as dependent variables. The results showed that the prediction effect of this model was ideal, the ratios of STD were all greater than 0.5, NMB were all close to 0 and EMR were all less than 19%. According to the relevant data from a research of indoor environment and childrens health in Tianjin, the concentration of DEHP was predicted. The EMR between average measured value and average predicted value was 7.7%, indicating that the prediction accuracy of the model was high. |
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