Description
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
Table of Contents
Part 1: Machine learning methods and connections between different parts. 1. Machine learning methods2. Connections between studies across different scales3. Summary and outlookPart 2: Machine learning in microscopic modelling of geo-materials.4. Machine-learning-enabled discrete element method5. Machine learning in micromechanics based virtual laboratory testing6. Integrating X-ray CT and machine learning for better understanding of granular materials7. Summary and outlookPart 3: Machine learning in constitutive modelling of geo-materials. 8. Thermodynamics-driven deep neural network as constitutive equations9. Deep active learning for constitutive modelling of granular materials10. Summary and outlookPart 4: Machine learning in design of geo-structures. 11. Deep learning for surrogate modelling for geotechnical risk analysis12. Deep learning for geotechnical optimization of designs13. Deep learning for time series forecasting in geotechnical engineering14. Summary and outlookPart 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 mapping19. Summary and outlook
