| With the expansion of social and economic development,the problem of air pollution has become increasingly serious.Air pollution is becoming more and more serious because of the increase of the main pollutants such as particulate matter,carbon monoxide,nitrogen dioxide,sulfur dioxide,it brings a heavy burden to environment,daily life and social development,threatening the sustainable development seriously.Because of the complex variability and massive historical data of air pollution,the traditional forecasting method excavates the historical data and get the useful information difficultly.Therefore,it is very meaningful to design air quality analysis system.This paper analyzes the changes of air pollutant concentration surrounding the monitoring and visualization of Hebei Province environmental air quality.The system which is based on the air pollution index data obtained by the air quality monitoring station of Shijiazhuang City is developed by Java.The Data acquisition part get the data from the atmosphere quality monitoring sites using WebService technology.The data analysis showed the data visualization,using histograms,line charts and maps which is provided by HightCharts and Echarts.The prediction part establishes prediction model of atmospheric quality in Shijiazhuang city by combining the Data mining technique with the BP neural network algorithm.First of all,The forecasting model of atmospheric uses the data mining clustering algorithm technology to find the factors which affect air quality firstly,Secondly,using 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 meet the requirement 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. |
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