AIMC Topic: Spatio-Temporal Analysis
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Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.
The Science of the total environment
Dec 18, 2024
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...
STCNet: Spatio-Temporal Cross Network with subject-aware contrastive learning for hand gesture recognition in surface EMG.
Computers in biology and medicine
Dec 13, 2024
This paper introduces the Spatio-Temporal Cross Network (STCNet), a novel deep learning architecture tailored for robust hand gesture recognition in surface electromyography (sEMG) across multiple subjects. We address the challenges associated with t...
Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.
Environmental pollution (Barking, Essex : 1987)
Nov 28, 2024
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...
MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.
Environmental monitoring and assessment
Nov 23, 2024
The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual ...
Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.
Environmental pollution (Barking, Essex : 1987)
Nov 22, 2024
Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 ...
High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.
Environmental pollution (Barking, Essex : 1987)
Nov 18, 2024
Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional f...
Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning.
The Science of the total environment
Oct 30, 2024
PM bound mercury (PBM) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when converted to methylmercury. Despite its importance, comprehensive macroanalyses of PBM on large sca...
Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting.
Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solu...
DyGraphformer: Transformer combining dynamic spatio-temporal graph network for multivariate time series forecasting.
Neural networks : the official journal of the International Neural Network Society
Oct 17, 2024
Transformer-based models demonstrate tremendous potential for Multivariate Time Series (MTS) forecasting due to their ability to capture long-term temporal dependencies by using the self-attention mechanism. However, effectively modeling the spatial ...
Modelling multivariate spatio-temporal data with identifiable variational autoencoders.
Neural networks : the official journal of the International Neural Network Society
Oct 9, 2024
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they c...
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