Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
A Causal Spatiotemporal Graph Neural Network

A Causal Spatiotemporal Graph Neural Network

Raghav, Yogita Yashveer
Kaushik, Bhawna

Paperback

General Computers

ISBN10: 663027900X
ISBN13: 9786630279009
Publisher: LAP Lambert Academic Publishing
Published: Jul 22 2026
Pages: 104
Weight: 0.33
Height: 0.25 Width: 6.00 Depth: 9.00
Language: English
A Causal Spatiotemporal Graph Neural Network for Interpretable PM2.5 Forecasting in Delhi NCR presents an advanced deep learning framework that combines causal inference, spatial relationships, and temporal dependencies to accurately predict PM2.5 air pollution levels in the Delhi NCR region. The book explores how Graph Neural Networks (GNNs) can model interactions among monitoring stations while identifying causal factors influencing air quality. It emphasizes forecasting accuracy, interpretability, and explainable AI, enabling policymakers and environmental agencies to better understand pollution patterns and make informed decisions for air quality management.

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