Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping - Softcover

Softcover

ISBN 10: 0443236631 ISBN 13: 9780443236631

Publisher: Elsevier, 2025

Synopsis

Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology.

• Introduces machine-learning techniques in the risk management of geo-hazards, particularly recent developments
• Covers a broader category of research and machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping
• Contains contributions from top researchers around the world, including authors from the UK, USA, Australia, Austria, China, and India

About this title

About the Authors

Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability.

Daichao Sheng is a distinguished professor and the head of School of Civil and Environmental Engineering. He has developed an internationally recognized profile in computational geomechanics including soft computing, unsaturated soils, geo-risk analysis and transport geotechnics. He has published 300+ peer-reviewed papers and two books, including 200+ papers in top geotechnical and computational mechanics journals. These publications now attract 1400+ citations per annum, with an H-Index of 48 in Scopus. His track record places him easily within the top handful of geomechanics professionals of his age worldwide. He has collaborated widely with Australian and international researchers in his field

Xuzhen He is a senior lecturer at UTS School of Civil and Environmental Engineering. He is an early career researcher and completed his undergraduate and PhD training at the world’s top universities (Tsinghua for his BSc and Cambridge for his PhD) and was awarded the John Winbolt Prize and the Raymond and Helen Kwok Scholarship from Cambridge University. He was awarded the Australian Research Council Discovery Early Career Researcher Award in 2021. His research interest lies mainly in computational geomechanics, and he has published 30+ high-quality journal papers in these areas.

From the Back Cover

Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. This book covers a broad category of research that machine-learning techniques that can be applied, from microscopic modelling to constitutive modelling, to physics-based numerical modelling, to regional susceptibility mapping. This will be a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology.

Edition details

• Publisher: Elsevier
• Publication date: 2025
• Language: English
• ISBN 10: 0443236631
• ISBN 13: 9780443236631
• Binding: Paperback
• Edition number: 1
• Number of pages: 376
• Editor: Pradhan