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
Machine Learning, Spatial Science and Natural Hazards: Case Studies and Resilience Strategies

Machine Learning, Spatial Science and Natural Hazards: Case Studies and Resilience Strategies

Halder, Somenath
Dey, Sumon

Hardcover

Series: Advances in Geographic Information Science

Environmental StudiesGeography

PREORDER - Expected ship date November 17, 2026

ISBN10: 3032295882
ISBN13: 9783032295880
Publisher: Springer
Published: Nov 17 2026
Pages: 370
Language: English
This contributed volume examines how machine learning (ML) and artificial intelligence (AI) are being used in hazard science, environmental management, and related policy studies. It brings together work that combines model-based outputs with ground-level data to study risk, monitoring, and decision-making across disciplines. The chapters here address natural hazard management, including fluvial and landslide processes, vegetation-erosion dynamics, air quality, forest fires, and coastal hazards. Watershed assessment, flood and erosion zonation, and approaches to regional and global monitoring using advanced datasets and models are also covered. Furthermore, the book explores links between environmental change, resource use, and social inequality, with attention to applications at multiple spatial scales.

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