Machine Learning in Geohazard Risk Prediction and Assessment
From Microscale Analysis to Regional Mapping
- 1 Edición - 2 de julio de 2025
- Última edición
- Editores: Biswajeet Pradhan, Daichao Sheng, Xuzhen He
- Idioma: Inglés
Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learni… Leer más
Descripción
Descripción
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.
Puntos claves
Puntos claves
- 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
De interès para
De interès para
Researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geologypopular science authors, primary/high school teachers, government officers for infrastructure, policymakers for regions with geo-risks
Índice
Índice
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
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
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 3 de julio de 2025
- Idioma: Inglés
Sobre los editores
Sobre los editores
BP
Biswajeet Pradhan
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.
Afiliaciones y experiencia
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, AustraliaDS
Daichao Sheng
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
Afiliaciones y experiencia
Distinguished Professor and Head of School of Civil and Environmental Engineering, Ultimo, New South Wales, AustraliaXH
Xuzhen He
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.
Afiliaciones y experiencia
Senior Lecturer, UTS School of Civil and Environmental Engineering, Ultimo, New South Wales, AustraliaVer libro en ScienceDirect
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