关键词: AQI等级, 预测, 堆栈降噪自编码, 优化

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 performance. 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.

Key words: AQI levels, prediction, Stacked Denoising Auto-Encoder（SDAE）, optimization