Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial corr...

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Bibliographic Details
Published in:	Environmental pollution (1987) Vol. 231; no. Pt 1; pp. 997 - 1004
Main Authors:	Li, Xiang, Peng, Ling, Yao, Xiaojing, Cui, Shaolong, Hu, Yuan, You, Chengzeng, Chi, Tianhe
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 01.12.2017
Subjects:	Air pollutant concentration predictions air pollutants Air Pollutants - analysis air pollution Air Pollution - statistics & numerical data air quality Beijing China Cities data collection Environmental Monitoring - methods Forecasting Long short-term memory neural network (LSTM NN) memory meteorological data model validation Models, Statistical Models, Theoretical monitoring Multiscale prediction neural networks Neural Networks (Computer) Particulate Matter - analysis particulates prediction public health Recurrent neural network Spatiotemporal correlation China Air pollutant concentration predictions Long short-term memory neural network (LSTM NN) Spatiotemporal correlation Multiscale prediction Recurrent neural network
ISSN:	0269-7491, 1873-6424, 1873-6424
Online Access:	Get full text
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