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

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.

Contributions draw on remote sensing, GIS, and statistical analysis to quantify environmental processes and assess policy responses. Case studies illustrate how these tools are applied in different contexts, alongside discussions of data integration and methodological design. The volume includes both theoretical and empirical work, offering perspectives on how ML- and AI-based approaches can be incorporated into research and practice.