计算机工程与应用2017，Vol.53Issue(21)：17-23,41,8.DOI:10.3778/j.issn.1002-8331.1705-0420
基于时空优化深度神经网络的AQI等级预测
AQI levels prediction based on deep neural network with spatial and temporal optimizations
摘要
Abstract
The existing air quality prediction models have lower precision, and sensitive to noisy data. Thus a new method is proposed for AQI levels prediction based on Stacked Denoising Auto-Encoders(SDAE)model. Firstly, the historical air quality and meteorological monitoring data of Wuhan city are taken as research object. SDAE model is established to study the characteristic expression of the original data layer by layer, and the last layer is connected with a classifier to tune the prediction model. The optimal set of hyper-parameters is found through improved grid search algorithm for multi-parameters. Then, the prediction is obtained from the test set. The indicators such as mean absolute error and mean square error between the predicted value and related actual value are used as the evaluation standards for forecasting perfor-mance. Compared with other network models, it can be proved that SDAE model has better predictive performance. Finally, the input data is optimized considering their spatial and temporal relations. Experimental results show that the spatial optimization based SDAE has the most improvement for predictive performance, and it can obtain more accurate predictions compared with the traditional methods.关键词
AQI等级/预测/堆栈降噪自编码/优化Key words
AQI levels/prediction/Stacked Denoising Auto-Encoder(SDAE)/optimization分类
信息技术与安全科学引用本文复制引用
董婷,赵俭辉,胡勇..基于时空优化深度神经网络的AQI等级预测[J].计算机工程与应用,2017,53(21):17-23,41,8.基金项目
中国空间技术研究院创新基金(No.CAST2014) （No.CAST2014）
湖北省科技支撑计划(No.2014BAA149) （No.2014BAA149）
中央高校基本科研业务费专项(No.2042016gf0023). （No.2042016gf0023）
