Isa Ebtehaj – författare

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation.

This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.

Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation.This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.- Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data- Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes- Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

This book offers a modern perspective on hazard and risk assessment in civil engineering, with special attention to sustainable infrastructure development. Through its comprehensive coverage of theoretical concepts and its thoughtful integration of sustainability into engineering practice, it serves as a valuable resource for professionals, researchers, and students.It bridges the gap between hazard and risk assessment and the principles of sustainable development, presenting a holistic approach to identifying, assessing, and mitigating risks associated with natural hazards while advancing sustainable infrastructure solutions. Beyond outlining core concepts, the book provides practical examples, user‑friendly code, and step‑by‑step software guidance. Readers will find numerous illustrations, diagrams, tables, and case studies that deepen understanding and support the application of risk assessment methodologies.Ultimately, the book is a guide to unlocking innovative solutions and shaping the future of civil engineering. Civil engineers, environmental scientists, policymakers, and anyone interested in understanding risk assessment will benefit from its insights into building a sustainable, resilient world.