scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

Numerical Regional Air Quality Forecast Tests Over the Mainland of China

2001 ◽  
pp. 1781-1786
Author(s):  
Junling An ◽  
Meiyuan Huang ◽  
Zifa Wang ◽  
Xinling Zhang ◽  
Hiromasa Ueda ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Quality Forecast ◽  
Regional Air Quality ◽  
The Mainland Of China ◽  
Quality Forecast

An Operational Evaluation of ETA-CMAQ Air Quality Forecast Model

2007 ◽  
pp. 590-598
Author(s):  
Daiwen Kang ◽  
Brian K. Eder ◽  
Rohit Mathur ◽  
Shaocai Yu ◽  
Kenneth L. Schere
Keyword(s):  
Air Quality ◽  
Forecast Model ◽  
Air Quality Forecast ◽  
Quality Forecast
Sign in / Sign up

Export Citation Format

Share Document