Urban big data fusion based on deep learning: An overview

J Liu, T Li, P Xie, S Du, F Teng, X Yang - Information Fusion, 2020 - Elsevier
Urban big data fusion creates huge values for urban computing in solving urban problems.
In recent years, various models and algorithms based on deep learning have been …

Diffusion models for time-series applications: a survey

L Lin, Z Li, R Li, X Li, J Gao - Frontiers of Information Technology & …, 2024 - Springer
Diffusion models, a family of generative models based on deep learning, have become
increasingly prominent in cutting-edge machine learning research. With distinguished …

Deciphering spatio-temporal graph forecasting: A causal lens and treatment

Y Xia, Y Liang, H Wen, X Liu, K Wang… - Advances in …, 2024 - proceedings.neurips.cc
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

Towards physics-informed deep learning for turbulent flow prediction

R Wang, K Kashinath, M Mustafa, A Albert… - Proceedings of the 26th …, 2020 - dl.acm.org
While deep learning has shown tremendous success in a wide range of domains, it remains
a grand challenge to incorporate physical principles in a systematic manner to the design …

[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 …

Deep air quality forecasting using hybrid deep learning framework

S Du, T Li, Y Yang, SJ Horng - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Air quality forecasting has been regarded as the key problem of air pollution early warning
and control management. In this article, we propose a novel deep learning model for air …

Urban flow prediction from spatiotemporal data using machine learning: A survey

P Xie, T Li, J Liu, S Du, X Yang, J Zhang - Information Fusion, 2020 - Elsevier
Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …

Deep spatio-temporal graph convolutional network for traffic accident prediction

L Yu, B Du, X Hu, L Sun, L Han, W Lv - Neurocomputing, 2021 - Elsevier
Traffic accidents usually lead to severe human casualties and huge economic losses in real-
world scenarios. Timely accurate prediction of traffic accidents has great potential to protect …

Learning from multiple cities: A meta-learning approach for spatial-temporal prediction

H Yao, Y Liu, Y Wei, X Tang, Z Li - The world wide web conference, 2019 - dl.acm.org
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …