基于MLP&ST模型的空气质量预测

doi:10.3969/j.issn.0255-8297.2022.02.012

Abstract

Abstract: Air quality prediction system is necessary in guarding human and environment health, and the historical air pollution data collected by monitoring stations in a city and advanced computer equipment makes it possible to forecast air quality in a data-driven way. However, most of the existing methods are suitable for forecasting the air quality in monitored areas, and only few are for unmonitored areas. In this paper, a joint training model MLP&ST (MLP&Spatial-Temporal) is proposed which takes the consideration of the comprehensive impact of meteorological factors, spatial correlation and time dependence on air quality, to predict the future air quality index (AQI) of unmonitored areas in Beijing. Compared with several other air quality forecast models, the optimized historical time step P value of 29 is experimentally obtained. Experimental results show that the hybrid model is superior to other models in performance indices (RMSE, MAE and MAPE) and has good ability of predictability.

Key words: air quality prediction, MLP&ST, air quality index(AQI)

CLC Number:

TP183

ZHENG Hong, CHENG Yunhui, HU Yangsheng, HUANG Jianhua. Air Quality Prediction Based on MLP&ST Model[J]. Journal of Applied Sciences, 2022, 40(2): 302-315.

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Air Quality Prediction Based on MLP&ST Model

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