AIMC Topic: Spatio-Temporal Analysis

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MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.

Environmental monitoring and assessment
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)
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)
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
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
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
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
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...

Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study.

PLoS computational biology
Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the...

Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique.

The Science of the total environment
Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. T...

SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.

Ultrasound in medicine & biology
OBJECTIVE: Ultrasound-based shear wave elastography offers estimation of tissue stiffness through analysis of the propagation of a shear wave induced by a stimulus. Displacement or velocity fields during the process can contain noise as a result of t...