Machine Learning in Geohazard Risk Prediction and Assessment

From Microscale Analysis to Regional Mapping
Erscheinungsjahr 2025
ISBN: 978-0-443-23663-1
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm, Gewicht: 790 g

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.

Autoren/Hrsg.

Pradhan, Biswajeet

Sheng, Daichao

He, Xuzhen

Fachgebiete

• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Geowissenschaften Umweltwissenschaften Naturgewalten & Katastrophen
• Geowissenschaften Geologie Geologie
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziale Gruppen/Soziale Themen Soziale Folgen von Katastrophen

Inhaltsverzeichnis

Part 1: Machine learning methods and connections between different parts.

1. Machine learning methods
2. Connections between studies across different scales
3. Summary and outlook

Part 2: Machine learning in microscopic modelling of geo-materials.
4. Machine-learning-enabled discrete element method
5. Machine learning in micromechanics based virtual laboratory testing
6. Integrating X-ray CT and machine learning for better understanding of granular materials
7. Summary and outlook

Part 3: Machine learning in constitutive modelling of geo-materials.

8. Thermodynamics-driven deep neural network as constitutive equations
9. Deep active learning for constitutive modelling of granular materials
10. Summary and outlook

Part 4: Machine learning in design of geo-structures.

11. Deep learning for surrogate modelling for geotechnical risk analysis
12. Deep learning for geotechnical optimization of designs
13. Deep learning for time series forecasting in geotechnical engineering
14. Summary and outlook

Part 5: Machine learning in geo-risk susceptibility mapping for regions of various sizes.

15. Deep learning and ensemble modeling of debris flows, mud flows and rockfalls.
16. Integrating machine learning and physical-based models in landslide susceptibility and hazard mapping.
17. Explainable AI (XAI) in landslide susceptibility, hazard, vulnerability and risk assessment.
18. New approaches for data collection for susceptibility mapping
19. Summary and outlook

Über Autor(innen)

He, Xuzhen
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.

Pradhan, Biswajeet
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.

Sheng, Daichao
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