MVG-KAN: Multi-View Geo-Wind Guided KAN for PM$_{2.5}$ Forecasting

2026-06-23 · Source: Artificial Intelligence · Field: Science & Research — Environmental Science & Earth Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

The MVG-KAN model is proposed for accurate short-term PM$_{2.5}$ forecasting, addressing limitations in existing spatio-temporal methods that often fail to comprehensively represent heterogeneous factors, particularly wind-direction-dependent pollutant transport. This model captures PM$_{2.5}$ evolution from three complementary views: local periodic regularity, station-wise residual temporal dynamics, and meteorological-environment-guided spatial dispersion. It employs a periodic-residual forecasting backbone to separate stable daily and weekly patterns. A Geo-Wind Graph, combining geographic distance decay with wind-direction- and wind-speed-aware transport, provides a physically motivated spatial prior. Additionally, a Temporal Kolmogorov-Arnold Network (TKAN) residual head learns station-wise nonlinear autoregressive correction from de-periodized PM$_{2.5}$ residuals and historical multi-pollutant sequences.

Key takeaway

For AI Scientists developing air quality models, MVG-KAN offers a robust approach to PM$_{2.5}$ forecasting. You should consider integrating multi-view factors like periodic regularity, station-wise dynamics, and geo-wind guided spatial dispersion. This method, leveraging KANs, can significantly improve the accuracy of short-term predictions, especially for complex pollutant transport scenarios, enhancing public health protection and urban environmental management efforts.

Key insights

MVG-KAN integrates multi-view factors, including geo-wind, with Kolmogorov-Arnold Networks for enhanced PM$_{2.5}$ forecasting.

Principles

• PM$_{2.5}$ variation involves periodic, station-specific, and meteorology-driven factors.
• Wind-direction-dependent transport is crucial for accurate pollutant modeling.
• Separating periodic patterns from residuals improves forecasting accuracy.

Method

The method separates periodic/residual PM$_{2.5}$ patterns, constructs a Geo-Wind Graph using distance and wind data, then applies a TKAN residual head for station-wise nonlinear autoregressive correction.

In practice

• De-periodize PM$_{2.5}$ data using a periodic-residual forecasting backbone.
• Build spatial priors for air quality models with geographic distance and wind data.
• Apply TKAN for local, nonlinear residual correction using multi-pollutant sequences.

Topics

• PM$_{2.5}$ Forecasting
• Kolmogorov-Arnold Networks
• Spatio-temporal Modeling
• Air Quality Prediction
• Meteorological Data
• Geo-Wind Graph

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.