376,73 €
418,59 €
-10% with code: EXTRA
Machine Learning in Geohazard Risk Prediction and Assessment
376,73 € 
- We will send in 10–14 business days.
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 susceptibili…
418.59
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- Publisher: Elsevier
- ISBN-10: 0443236631
- ISBN-13: 9780443236631
- Format: 19.1 x 23.4 x 2 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Machine Learning in Geohazard Risk Prediction and Assessment (e-book) (used book) | bookbook.eu
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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.EXTRA 10 % discount with code: EXTRA
376,73 €
418,59 €
We will send in 10–14 business days.
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- Publisher: Elsevier
- ISBN-10: 0443236631
- ISBN-13: 9780443236631
- Format: 19.1 x 23.4 x 2 cm, minkšti viršeliai
- Language: English English
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.
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