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 …

Frequency-domain MLPs are more effective learners in time series forecasting

K Yi, Q Zhang, W Fan, S Wang… - Advances in …, 2024 - proceedings.neurips.cc
Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …

Air-pollution prediction in smart city, deep learning approach

A Bekkar, B Hssina, S Douzi, K Douzi - Journal of big Data, 2021 - Springer
Over the past few decades, due to human activities, industrialization, and urbanization, air
pollution has become a life-threatening factor in many countries around the world. Among …

FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective

K Yi, Q Zhang, W Fan, H He, L Hu… - Advances in …, 2024 - proceedings.neurips.cc
Multivariate time series (MTS) forecasting has shown great importance in numerous
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …

Deep spatio-temporal residual networks for citywide crowd flows prediction

J Zhang, Y Zheng, D Qi - Proceedings of the AAAI conference on …, 2017 - ojs.aaai.org
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, such as inter-region …

[HTML][HTML] An LSTM-based aggregated model for air pollution forecasting

YS Chang, HT Chiao, S Abimannan, YP Huang… - Atmospheric Pollution …, 2020 - Elsevier
During the past few years, severe air-pollution problem has garnered worldwide attention
due to its effect on health and wellbeing of individuals. As a result, the analysis and …

Airformer: Predicting nationwide air quality in china with transformers

Y Liang, Y Xia, S Ke, Y Wang, Q Wen, J Zhang… - Proceedings of the …, 2023 - ojs.aaai.org
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the
barriers to economic growth. Forecasting air quality has become an increasingly important …

Long short-term memory-Fully connected (LSTM-FC) neural network for PM2. 5 concentration prediction

J Zhao, F Deng, Y Cai, J Chen - Chemosphere, 2019 - Elsevier
People have been suffering from air pollution for a decade in China, especially from PM 2.5
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …

[PDF][PDF] Geoman: Multi-level attention networks for geo-sensory time series prediction.

Y Liang, S Ke, J Zhang, X Yi, Y Zheng - IJCAI, 2018 - researchgate.net
Numerous sensors have been deployed in different geospatial locations to continuously and
cooperatively monitor the surrounding environment, such as the air quality. These sensors …