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

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ISBN 10: 0443236631 ISBN 13: 9780443236631

Casa editrice: Elsevier, 2025

Riassunto

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.

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Informazioni sugli autori

Biswajeet Pradhan is a distinguished professor at UTS School of Civil and Environmental Engineering. He is an international expert in data-driven modelling and a pioneer in combining spatial modelling with statistical and machine learning models for natural hazard predictions including landslides. He has a track record of outstanding research outputs, with over 600 journal articles. He is a highly interdisciplinary researcher with publications across 12 areas, listed as having ‘Excellent’ international collaboration status. He has been a Highly Cited Researcher for five consecutive years (2016-2020) and ranks fifth in the field of Geological & Geoenvironmental Engineering.

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.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Elsevier
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 0443236631
• ISBN 13: 9780443236631
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 376
• Redattore: Pradhan
• Contatto del produttore: ELSEVIER; https://www.elsevier.com/es-es; ; Mooswaldallee 6; Freiburg; 79108; Germania