摘要
文章基于2014至2021年中国368个城市空气质量数据,运用深度学习模型(包括SCINet、Informer、LSTM、GRU、RNN)构建空气质量预测模型。平台通过采用Qt Designer和Python开发,并结合ETL、数据可视化等技术,实现空气质量预测可视化。平台支持精准预测空气质量指数,分析污染源影响,用户可自选区域和模型进行预测,并可视化呈现预测性能。平台适用于环保、气象、科研机构及公众,提供高效、准确的空气质量监测与预测工具。
Based on the air quality data of 368 cities in China from 2014 to 2021,Deep Learning models(including SCINet,Informer,LSTM,GRU and RNN)are used to construct air quality prediction models.The platform is developed by using Qt Designer and Python,and combined with technologies such as ETL and data visualization to achieve the visualization of air quality prediction.The platform supports accurate prediction of the air quality index and analysis of the impact of pollution sources.Users can select their own regions and models for prediction and visualize the prediction performance.The platform is suitable for environmental protection,meteorological,scientific research institutions and the public,providing efficient and accurate air quality monitoring and prediction tools.
作者
张斌
方玲
莫博文
孙刘洋
覃业梅
ZHANG Bin;FANG Ling;MO Bowen;SUN Liuyang;QIN Yemei(School of Intelligent Engineering and Intelligent Manufacturing,Hunan University of Technology and Business,Changsha 410205,China)
出处
《现代信息科技》
2025年第5期62-65,71,共5页
Modern Information Technology
基金
国家级大学生创新创业计划项目(S202310554004)。
