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[HTML][HTML] Machine learning methods in weather and climate applications: A survey
With the rapid development of artificial intelligence, machine learning is gradually becoming
popular for predictions in all walks of life. In meteorology, it is gradually competing with …
popular for predictions in all walks of life. In meteorology, it is gradually competing with …
[HTML][HTML] Unsupervised machine learning in urban studies: A systematic review of applications
Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Largest: A benchmark dataset for large-scale traffic forecasting
Road traffic forecasting plays a critical role in smart city initiatives and has experienced
significant advancements thanks to the power of deep learning in capturing non-linear …
significant advancements thanks to the power of deep learning in capturing non-linear …
Privacy-preserved data sharing towards multiple parties in industrial IoTs
The effective physical data sharing has been facilitating the functionality of Industrial IoTs,
which is believed to be one primary basis for Industry 4.0. These physical data, while …
which is believed to be one primary basis for Industry 4.0. These physical data, while …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Future directions in human mobility science
We provide a brief review of human mobility science and present three key areas where we
expect to see substantial advancements. We start from the mind and discuss the need to …
expect to see substantial advancements. We start from the mind and discuss the need to …
A hybrid-convolution spatial–temporal recurrent network for traffic flow prediction
Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …