£124.81

Elsevier Machine Learning in Geohazard Risk Prediction Book

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Description

Explore the intersection of data science and earth sciences with this academic resource from Elsevier. Machine Learning in Geohazard Risk Prediction and Assessment covers the essential methodologies required to move from microscale analysis to regional mapping. This text is designed for higher education environments, providing a structured approach to understanding how machine learning applications can improve the accuracy of geohazard assessments. As geohazard monitoring becomes more data-driven, having a reliable guide on predictive modeling is vital for students and researchers in the Earth Sciences field. This book addresses the technical requirements for mapping risks and analyzing small-scale data to create broader regional models. It serves as a foundational tool for those studying how modern computational techniques can be applied to natural hazard prediction and risk management strategies.

Key Features

Covers the full spectrum of geohazard analysis from microscale studies to large-scale regional mapping applications.

Provides a specialized focus on machine learning methodologies tailored for Earth Sciences and risk prediction.

Designed as a higher education resource for students and researchers studying advanced geohazard assessment.

Published by Elsevier to ensure academic quality and professional standards in science and nature subjects.

Explains the transition of data from small-scale analysis to comprehensive regional risk models.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
04 July 2025
Listed Since
17 January 2024

Barcode

No barcode data available

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