scholarly journals Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 38186-38199 ◽  
Author(s):  
Ping-Wei Soh ◽  
Jia-Wei Chang ◽  
Jen-Wei Huang
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Temporal Relations ◽  
Air Quality Prediction

Statistical oxidant air quality prediction model for land use and transportation planning

1977 ◽  
Vol 11 (5) ◽  
pp. 449-458 ◽  
Author(s):  
J.M. Caporaletti ◽  
L.N. Myrabo ◽  
P. Schleifer ◽  
A. Stanonik ◽  
K.R. Wilson
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Prediction Model ◽  
Transportation Planning ◽  
Quality Prediction ◽  
Air Quality Prediction

scholarly journals Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14765-14778
Author(s):  
Ichrak Mokhtari ◽  
Walid Bechkit ◽  
Herve Rivano ◽  
Mouloud Riadh Yaici
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Learning Architectures

scholarly journals Deep Learning Model for Air Quality Prediction Based on Big Data

2021 ◽  
pp. 170-175
Author(s):  
P. Parkavi ◽  
S. Rathi
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Pollution Monitoring ◽  
The Internet ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
The Internet Of Things

Air pollution and its harm to human health has become a serious problem in many cities around the world. In recent years, research interests in measuring and predicting the quality of air around people has spiked. Since the Internet of things has been widely used in different domains to improve the quality for people by connecting multiple sensors. In this work an IOT based air pollution monitoring with prediction system is proposed. The internet of Things is a action interrelated computing devices that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. The deep learning algorithm approach is to evaluate the accuracy for the prediction of air pollution. The main objective of the project is used to predict the air Quality. The large dataset works with LSTM for better air quality prediction. The prediction accuracy of air quality with LSTM, the evaluation indicator Root means square error is chosen to measure performance.

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