Deep learning for air pollutant concentration prediction: A review

B Zhang, Y Rong, R Yong, D Qin, M Li, G Zou… - Atmospheric …, 2022 - Elsevier
Air pollution has become one of the critical environmental problem in the 21st century and
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …

Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective

A Kaginalkar, S Kumar, P Gargava, D Niyogi - Urban Climate, 2021 - Elsevier
Cities foster economic growth. However, growing cities also contribute to air pollution and
climate change. The paper provides a perspective regarding the opportunity available in …

Predicting next hour fine particulate matter (PM2. 5) in the Istanbul Metropolitan City using deep learning algorithms with time windowing strategy

B Eren, İ Aksangür, C Erden - Urban Climate, 2023 - Elsevier
Poor air quality has various detrimental physical and mental effects on human health and
quality of life. In particular, PM 2.5 air pollution has been associated with cardiovascular and …

24-Hour prediction of PM2. 5 concentrations by combining empirical mode decomposition and bidirectional long short-term memory neural network

M Teng, S Li, J Xing, G Song, J Yang, J Dong… - Science of The Total …, 2022 - Elsevier
Accurate prediction of the future PM 2.5 concentration is crucial to human health and
ecological environmental protection. Nowadays, deep learning methods show advantages …

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

J Bi, KE Knowland, CA Keller, Y Liu - Environmental science & …, 2022 - ACS Publications
Forecasting ambient PM2. 5 concentrations with spatiotemporal coverage is key to alerting
decision makers of pollution episodes and preventing detrimental public exposure …

[HTML][HTML] Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system

Y Li, L Ma, J Huang, M Disse, W Zhan, L Li… - Environmental Science …, 2024 - Elsevier
The process-based water system models have been transitioning from single-functional to
integrated multi-objective and multi-functional since the worldwide digital upgrade of urban …

A balanced social LSTM for PM2. 5 concentration prediction based on local spatiotemporal correlation

L Shi, H Zhang, X Xu, M Han, P Zuo - Chemosphere, 2022 - Elsevier
Reliable prediction for the concentration of PM 2.5 has become a hot topic in pollution
prevention. However, the prediction for PM 2.5 concentration remains a challenge, one of …

Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data

S Wang, J McGibbon, Y Zhang - Environmental Pollution, 2024 - Elsevier
Accurately predicting air pollutants, especially in urban areas with well-defined spatial
structures, is crucial. Over the past decade, machine learning techniques have been widely …

On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

Z Ren, X Ji - Atmospheric Pollution Research, 2023 - Elsevier
In recent years, air pollution has attracted considerable attention worldwide. As an effective
air protection method, the accurate prediction of air pollutants can help provide an early …

PM2. 5 concentration prediction system combining fuzzy information granulation and multi-model ensemble learning

Y Chen, J Wang, R Li, J Gao - Journal of Environmental Sciences, 2024 - Elsevier
With the rapid development of economy, air pollution caused by industrial expansion has
caused serious harm to human health and social development. Therefore, establishing an …