Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation-Reference-Cited by-同舟云学术

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Published:2023-06 Issue:6 Volume:14 Page:101766
ISSN:1309-1042
Container-title:Atmospheric Pollution Research
language:en
Short-container-title:Atmospheric Pollution Research

Author:

Ehteram Mohammad,Najah Ahmed Ali^ORCID,Khozani Zohreh Sheikh,El-Shafie Ahmed

Publisher

Elsevier BV

Subject

Atmospheric Science,Pollution,Waste Management and Disposal

Reference38 articles.

1. Structural crack detection using deep convolutional neural networks;Ali;Autom. ConStruct.,2022

2. Multi hours ahead prediction of surface ozone gas concentration: robust artificial intelligence approach;AlOmar;Atmos. Pollut. Res.,2020

3. MLP based predictive model for surface ozone concentration over an urban area in the Gangetic West Bengal during pre-monsoon season;Chattopadhyay;J. Atmos. Sol. Terr. Phys.,2019

4. A long short-term memory neural network based Wiener process model for remaining useful life prediction;Chen;Reliab. Eng. Syst. Saf.,2022

5. Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China;Cheng;J. Environ. Manag.,2021

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