文献类型：期刊文献

英文题名：Air Quality Prediction Based on Integrated Dual LSTM Model

作者：Chen, Hongqian[1];Guan, Mengxi[1];Li, Hui[2]

第一作者：Chen, Hongqian

通讯作者：Li, H[1]

机构：[1]Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China;[2]Beijing Union Univ, Management Coll, Beijing 100101, Peoples R China

第一机构：Beijing Technol & Business Univ, Sch Comp Sci & Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Management Coll, Beijing 100101, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2021

卷号：9

起止页码：93285-93297

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20213310761942);Scopus(收录号：2-s2.0-85112210002);WOS:【SCI-EXPANDED(收录号:WOS:000673923500001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 31701517, in part by the Beijing Philosophy and Social Science Foundation under Grant 17GLC060 and Grant 20GLB032, in part by the Academic Research Project of Beijing Education Commission under Grant SZ202111417021, and in part by the Academic Research Projects of Beijing Union University under Grant ZB10202005 and Grant JS10202006.

语种：英文

外文关键词：Air quality; Atmospheric modeling; Predictive models; Data models; Meteorology; Time series analysis; Forecasting; Air quality prediction; integrated dual model; LSTM model with attention mechanism; Seq2Seq technology; XGBoosting tree

摘要：Air quality prediction is an important reference for meteorological forecast and air controlling, but over fitting often occurs in prediction algorithms based on a single model. Aiming at the complexity of air quality prediction, a prediction method based on integrated dual LSTM (Long Short-Term Memory) model was proposed in this paper. Firstly, the Seq2Seq (Sequence to Sequence) technology is used to establish a single-factor prediction model which can obtain the predicted value of each component in air quality data, independently. Each component of air quality is regarded as time series data in the forecasting process. Then, the LSTM model with attention mechanism is used as the multi-factor prediction model. The influencing factors of air quality, like the data of neighboring stations and weather data, are considered in the model. Finally, XGBoosting (eXtreme Gradient Boosting) tree is used to integrate two models. The final prediction results can be obtained by accumulating the predicted values of the optimal subtree nodes. Through evaluation and analysis using five evaluation methods, the proposed method has better performance in terms of error and model expression power. Compared with other various models, the precision of prediction data has been greatly improved in our model.