Machine Learning, Spatial Science and Natural Hazards

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. 

Dr. Somenath Halder is an Assistant Professor at the Department of Geography in Kaliachak College, University of Gour Banga, West Bengal, India. He has completed his PG from Department of Geography and Applied Geography, University of North Bengal, India, and PhD from Department of Geography, Visva-Bharati, India. Dr. Halder's field of interest includes multi-dimensional data modeling connecting to socio-political agenda, conflicting issues with laws and ecology, community practices, crisis management, spatio-temporal analysis, environmental management, policy science, climate change, watershed management, hydrological modeling, geospatial data analysis, and GIS applications with more than 15 academic years of experience. Dr. Halder has published more than 48 research articles in peer-reviewed reputed journals. Meanwhile, he performs his academic endeavor as peer-reviewer in number of Scopus and Web of Science indexed prestigious journals like, Modeling Earth System and Environment, Journal of Environment, Development and Sustainability, Internal Journal of Geoheritage and Parks, GeoJournal, South Asian Survey, Research in Globalization, Current Psychology, Sage Open, and may more. Recently Dr. Halder published an edited book entitled Advancement of GI-Science and Sustainable Agriculture: A Multi-Dimensional Approach, New Advancements in Geomorphological Research: Issues & Challenges in Quantitative Spatial Science, and Progress in Multicriteria Decision Making Models: A New Paradigm to hazard Monitoring jointly with Dr. Jayanta Das from Springer Nature. Dr. Sumon Dey is an Assistant Professor (Scale-I) at the Department of Computer Applications in Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India. He has completed his Postgraduate and PhD degrees from the Department of Computer Science and Technology at the University of North Bengal, India. Dr. Dey's PhD thesis covers the broader theme of Landslide Susceptibility Assessment and Zonation in the Darjeeling Himalayan Region using RS-GIS and Machine Learning-based Techniques. His research interests include disaster susceptibility assessment and mapping, optimization techniques, statistical modeling, ensemble learning, explainable artificial intelligence, and remote sensing and geographic information systems, with more than 6 years of academic experience. Dr. Sumon Dey has published several scholarly articles in international journals of repute (indexed in Science Citation Index Expanded and Scopus) and book chapters (indexed in Scopus), focusing mainly on landslide susceptibility zonation and mapping in the Eastern Himalayas and the Western Ghats.