Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques-Reference-Cited by-同舟云学术

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Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques

Published:2021-12 Issue: Volume:75 Page:103372
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Sun Haochen,Fung Jimmy C.H.,Chen Yiang,Chen Wanying,Li Zhenning,Huang Yeqi,Lin Changqing,Hu Mingyun,Lu Xingcheng

Funder

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference52 articles.

1. Time series analysis of air pollution in Bengaluru using ARIMA model;Abhilash,2018

2. An ensemble long short-term memory neural network for hourly PM2. 5 concentration forecasting;Bai;Chemosphere,2019

3. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models;Bocquet;Atmospheric Chemistry and Physics,2015

4. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models;Box;Journal of the American Statistical Association,1970

5. A review of artificial neural network models for ambient air pollution prediction;Cabaneros;Environmental Modelling & Software,2019

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1. Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia;Results in Engineering;2024-02

2. Assessment of tropospheric ozone simulations in a regional chemical transport model using GEOS-Chem outputs as chemical boundary conditions;Science of The Total Environment;2024-01

3. Study and development of hybrid and ensemble forecasting models for air quality index forecasting;Expert Systems;2023-09-12

4. A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction;Atmospheric Pollution Research;2023-08

5. Deep learning-based multi-target regression for traffic-related air pollution forecasting;Machine Learning with Applications;2023-06

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