基于深度学习的空气质量综合分析系统

Air Quality Comprehensive Analysis System Based on Deep Learning

空气质量指数(AQI)是衡量环境空气质量的重要指标,能够方便地获取空气质量监测和预报数据具有重要的研究价值。采用网络爬虫技术采集了苏州市2014-2020年的历史空气质量数据,基于信息增益(IG)和长短期记忆网络(LSTM)计算各污染物对AQI的信息增益,并开展空气质量预测。实验结果表明:与LSTM模型相比,提出的基于信息增益的LSTM模型能够更准确地预报AQI。此外,建立了空气质量综合分析系统,功能丰富直观,为政府和公众提供科学依据。

The Air Quality Index(AQI)is an important indicator for measuring the quality of environmental air.The ability to easily obtain air quality monitoring and forecasting data is of significant research value.Historical air quality data from Suzhou City from 2014 to 2020 were collected using web crawling technology.Based on Information Gain(IG)and Long Short-Term Memory networks(LSTM),the information gain of each pollutant on the AQI was calculated,and air quality forecasting was conducted.The experimental results show that,compared to the LSTM model,the proposed LSTM model based on information gain can predict the AQI more accurately.In addition,an air quality comprehensive analysis system has been established,which is rich in functions and intuitive,providing a scientific basis for the government and the public.