arXiv:2606.24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution. Graph Neural Networks (GNNs) effectively model spatiotemporal dependencies, but predefined graphs limit adaptability, and some datasets complicate learning. This study introduces a graph construction method based on a confusion matrix from a supervised learning process to dynamically capture inter-class relationships. Additionally, a hybrid loss function that combines energy distance and Huber loss is applied to address the vanishing gradient problem and improve
The increasing availability of spatiotemporal data and advancements in Graph Neural Networks are enabling more sophisticated approaches to environmental monitoring.
Accurate and adaptable pollution prediction is critical for public health, environmental policy, and resource management, impacting large populations and economic activities.
The dynamic graph construction and hybrid loss functions proposed could lead to more robust and generalizable AI models for environmental forecasting, reducing reliance on pre-defined graph structures.
- · Environmental agencies
- · Smart city initiatives
- · Machine learning researchers
- · Public health organizations
- · Traditional pollution monitoring methods
- · Systems with static data models
Improved early warning systems for air quality events.
Better targeted interventions and policy adjustments to mitigate pollution, potentially reducing healthcare burdens.
Enhanced integration of AI into urban planning and environmental governance, leading to more resilient and sustainable cities.
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Read at arXiv cs.LG