An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network
Ruobo Xin, Zhifang Jiang, Ning Li, Lujian Hou
Available Online May 2014.
- https://doi.org/10.2991/iccia.2012.100How to use a DOI?
- prediction, air quality, BP neural network
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ruobo Xin AU - Zhifang Jiang AU - Ning Li AU - Lujian Hou PY - 2014/05 DA - 2014/05 TI - An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network BT - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012) PB - Atlantis Press SP - 415 EP - 418 SN - 1951-6851 UR - https://doi.org/10.2991/iccia.2012.100 DO - https://doi.org/10.2991/iccia.2012.100 ID - Xin2014/05 ER -