A new air quality forecasting model using data mining and artificial neural network

Abstract

In this paper, we have established a forecasting model of atmospheric pollution. The forecasting model which combines with data mining techniques and BP neural network algorithm is based on the monitoring data of air pollution obtained from Shijiazhuang air quality monitoring stations. Firstly this model uses the data mining technology to find the factors which affect air quality. Secondly it uses these factors data to train the neural network. Finally, the evaluation test of the forecasting model is evaluated. The results show that: The atmospheric quality forecasting model established in this paper can well meet the needs of practical application, because it has higher forecasting accuracy. The forecasting model improves the effectiveness and practicability, and can provide more reliable decision evidence for environmental protection departments.