银川市空气污染指数的分析与预测

Analysis and Forecasting of Air Pollution Index in Yinchuan

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-预测-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.

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.