| 银川市空气污染指数的分析与预测 |
| |
| 引用本文： | 李媛.银川市空气污染指数的分析与预测[J].宁夏大学学报(自然科学版),2012(4):416-419. |
| |
| 作者姓名： | 李媛 |
| |
| 作者单位： | [1]宁夏大学农学院,宁夏银川750021 [2]宁夏大学资源环境学院,宁夏银川750021 |
| |
| 基金项目： | 国家自然科学基金资助项目（41261021） |
| |
| 摘 要： | 空气污染指数（air pollution index，API）是评价空气质量状况的有效手段．在分析银川市API变化特征的基础上，将小波分析与BP神经网络相结合，分别采用分解一预测一重构法和小波函数替代法对银川市API值进行了预测．结果表明：银川市API呈现年际下降，月际周期波动的特点；相对于其他小波，采用db10对数据进行分解、预测、重构后获得的结果最好；分解预测重构模型的预测精度较高，优于小波函数替代模型，适用于银川市空气污染指数的预测．
|
| 关 键 词： | 空气污染指数 小波分析 BP神经网络 |
Analysis and Forecasting of Air Pollution Index in Yinchuan |
| |
| Li Yuan.Analysis and Forecasting of Air Pollution Index in Yinchuan[J].Journal of Ningxia University(Natural Science Edition),2012(4):416-419. |
| |
| Authors: | Li Yuan |
| |
| Institution: | Li Yuan(1. School of Agriculture, Ningxia University, Yinchuan 750021, China; 2. School of Resources and Environment, Ningxia University, Yinchuan 750021, China) |
| |
| Abstract: | Air pollution index （API） is an effective way to assess air quality. On the basis of analysis change characteristics of API in Yinchuan, wavelet analysis and BP neural network are combined to set up ＂decomposition-forecast-reconstruction＂ model and ＂wavelet function replacement＂ model to foreeast API of Yinchuan. Result shows the ehange characteristics of API in Yinchuan are that it decrease from 2001 to 2011 totally and fluctuate periodically from January to December; The outcome which is acquired through decomposing data using dbl0, forecasting and reconstructing are better than other wavelet; The forecast accuracy of ＂decompose-forecast-reconstruction＂ method is high and better than ＂wavelet function replace＂ model. So, it is an effective method to forecast API of Yinchuan. |
| |
| Keywords: | air pollution index wavelet analysis BP neural network |
| 本文献已被 维普 等数据库收录！ |
