摘要
以华北某市1986～2000年各季度的降尘月均值监测数据为例,采用最优化理论,对大气质量的中长期数值预测方法进行组合研究,并与一些单一预测方法结果进行对比,旨在寻找既可以达到较高的精度要求又可以避免应用大量参数的组合预测模型.
Taking seasonal month-average Particulate Matter (PM) data of a city located in North China from 1986 to 2000 as a study case, based on the principle of optimization theory, mid and long-term prediction methods of air quality are studied and compared with some single prediction methods.The aim of this research is to find a combination prediction model which can both get better precision results and avoid taking too many parameters.
出处
《天津工业大学学报》
CAS
2005年第3期54-57,61,共5页
Journal of Tiangong University
基金
国家自然科学基金资助项目(50278062)
关键词
大气质量
中长期预测
时间序列分析法
灰色系统理论法
人工神经网络
组合预测模型
air quality
mid and long-term prediction
time series analysis method
grey system theory
artificial neural netwrok
combination prediction model
