| 基于基因表达式编程的PM2．5浓度预测模型研究 |
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| 引用本文： | 刘小生,李 胜,赵相博. 基于基因表达式编程的PM2．5浓度预测模型研究[J]. 南方冶金学院学报, 2013, 0(5): 1-5 |
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| 作者姓名： | 刘小生 李 胜 赵相博 |
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| 作者单位： | （江西理工大学建筑与测绘工程学院;江西;赣州;341000） |
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| 基金项目： | 国家自然科学基金资助项目（41261093） |
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| 摘 要： | 鉴于PM2.5浓度影响因素的复杂性，以及传统预测方法中存在的困难和不足，文中运用基因表达式编程算法，利用北京市2013年3月至4月的PM2.5日平均浓度值以及同步日平均污染物和气象数据，建立了PM2.5浓度预测模型．通过与灰色理论预测模型、BP神经网络预测模型的对比实验分析，发现基于基因表达式编程的预测模型所得到的预测值与实际值之间的误差最小，更能准确地反映样本数据之间的映射关系，预测精度明显高于其他2种预测模型．
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| 关 键 词： | 基因表达式编程 PM2 5 预测模型 空气污染 |
A Study on the prediction model of PM2.5 concentration based on gene expression programming |
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| LIU Xiao-sheng,LI Sheng,ZHAO Xiang-bo. A Study on the prediction model of PM2.5 concentration based on gene expression programming[J]. Journal of Southern Institute of Metallurgy, 2013, 0(5): 1-5 |
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| Authors: | LIU Xiao-sheng LI Sheng ZHAO Xiang-bo |
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| Affiliation: | (School of Architectural and Surveying ＆ Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China) |
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| Abstract: | Because of the complexity of influence factors of the PM2.5 concentration, as well as the difficulty contrasting with the gray theory prediction model, BP neural network prediction model, found that the error between predicted value and actual value in the prediction model based on gene expression programming is the minimum, which can more accurately reflect the mapping relationship between sample data, the prediction accuracy is significantly higher than other two kinds of prediction models. |
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| Keywords: | gene expression programming PM2.5 prediction model air pollution |
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