Abstract
In recent years, with the rapid development of China’s economy and the continuous improvement of people’s quality of life, air pollution caused by a large amount of energy consumption has become increasingly serious. Air quality index (AQI) has become an important basis to measure air quality. At present, the research on air quality assessment and prediction methods has become increasingly active at home and abroad, which is of great significance to guide people’s production and life. In this paper, Taking Shijiazhuang, Hebei Province as an example and using the XGBoost model of the machine learning ensemble algorithm, regression fitting was performed on the six pollutant concentrations that currently mainly affect air quality, and the hourly prediction of AQI was achieved.The trained model has lower mean absolute error (MAE) and higher correlation coefficient (R-square), which improves the prediction ability of urban air quality prediction, provides a new idea for urban air quality prediction, and has a broad application prospect in the future urban air quality prediction.
Highlights
In recent years, China's economy has developed steadily and rapidly, the industrialization of urban and rural areas and the living standards of residents have been greatly improved, and the scale of industry and transportation has continued to expand
The air pollution problems caused by the main pollutants such as inhalable particles (PM10, PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide, nitrogen oxide, carbon monoxide (CO), ozone (O3) are becoming increasingly serious, It has gradually become a major livelihood issue that the people are increasingly concerned about[1]
With the continuous improvement of the urban ambient air quality monitoring system, the monitoring data is growing rapidly, and a large amount of historical monitoring data has been accumulated, which provides an important basis for the analysis and control of the urban ambient air quality
Summary
China's economy has developed steadily and rapidly, the industrialization of urban and rural areas and the living standards of residents have been greatly improved, and the scale of industry and transportation has continued to expand. Shijiazhuang, the capital of Hebei Province, as a key pollution city in China, how to make correct prediction and assessment of future air quality changes, and achieve effective control of regional ambient air quality has become an important research topic of practical significance. With the continuous improvement of the urban ambient air quality monitoring system, the monitoring data is growing rapidly, and a large amount of historical monitoring data has been accumulated, which provides an important basis for the analysis and control of the urban ambient air quality. In view of the above background, this paper studies XGBoost model based on machine learning boosting ensemble algorithm, analyzes and cleans the historical monitoring data of air quality, obtains the prediction model through machine training, so as to grasp the trend of air quality change, and provides scientific and reasonable decision-making information
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