气候变化领域集成服务门户(CCPortal): Real-time hourly ozone prediction system for Yangtze River Delta area using attention based on a sequence to sequence model

CCPortal

DOI	10.1016/j.atmosenv.2020.117917
	Real-time hourly ozone prediction system for Yangtze River Delta area using attention based on a sequence to sequence model
	Jia P.; Cao N.; Yang S.
发表日期	2021
ISSN	1352-2310
卷号	244
英文摘要	The Yangtze River Delta (YRD) area is becoming increasingly polluted with ground level ozone, making the prediction of ozone particularly important. This study uses a deep learning approach to forecast ozone concentrations over the YRD region of eastern China. We propose an attention-based sequence to sequence model for ozone concentration prediction, which addresses the dynamic, spatial, temporal, and nonlinear characteristics of multivariate time series data by gated recurrent unit based encoder-forecaster architecture. Through multivariate time series forecasting experiments for ozone concentration, we show that the proposed model is easier and performs better than the weather research and forecasting model with chemistry based forecasting system. Furthermore, we show that the predicted ozone concentration can be matched with the ground truth value under single-timestep and multi-timestep forward forecasting conditions. The experiment results show that the seq2seq model is capable of reliably predicting ozone concentration with a high level of accuracy. The root mean square error of 1- h ozone forecast is 12.40 μg/m³ and the mean absolute error of 1- h ozone forecast is 9.27 μg/m³ on the test dataset. © 2020 Elsevier Ltd
关键词	Air quality model Attention Ozone prediction Sequence to sequence deep learning Yangtze river delta
语种	英语
scopus关键词	Deep learning; Mean square error; Ozone; Statistical tests; Time series; Ground-level ozone; Mean absolute error; Multivariate time series; Nonlinear characteristics; Ozone concentration; Root mean square errors; Weather research and forecasting models; Yangtze river delta; Weather forecasting; ozone; concentration (composition); forecasting method; model; ozone; real time; spatiotemporal analysis; Article; China; deep learning; deep neural network; environmental monitoring; forecasting; ground level ozone; meteorology; pollutant; priority journal; quality control; river; summer; time series analysis; China; Yangtze River
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248840
作者单位	Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China
推荐引用方式 GB/T 7714	Jia P.,Cao N.,Yang S.. Real-time hourly ozone prediction system for Yangtze River Delta area using attention based on a sequence to sequence model[J],2021,244.
APA	Jia P.,Cao N.,&Yang S..(2021).Real-time hourly ozone prediction system for Yangtze River Delta area using attention based on a sequence to sequence model.ATMOSPHERIC ENVIRONMENT,244.
MLA	Jia P.,et al."Real-time hourly ozone prediction system for Yangtze River Delta area using attention based on a sequence to sequence model".ATMOSPHERIC ENVIRONMENT 244(2021).