Machine Learning in Geohazard Risk Prediction and Assessment (eBook, ePUB)

From Microscale Analysis to Regional Mapping
Redaktion: Pradhan, Biswajeet; He, Xuzhen; Sheng, Daichao

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 325
• Erscheinungstermin: 1. Juli 2025
• Englisch
• ISBN-13: 9780443236648
• Artikelnr.: 74796190

Inhaltsangabe

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