Machine Learning in Earth, Environmental and Planetary Sciences - Hossein Bonakdari

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Auteur(s) : Hossein Bonakdari - Isa Ebtehaj - Joseph D Ladouceur

Editeur : Elsevier Science

Langue : Anglais

Parution : 01/06/2023

Format : Moyen, de 350g à 1kg

Nombre de pages : 388

Expédition : 1066

Dimensions : 21.3 x 27.6 x 2.4

ISBN : 9780443152849

Résumé :

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.

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Biographie:

Dr. Hossein Bonakdari is a distinguished professor in the Department of Civil Engineering at the University of Ottawa, specializing in mathematical modeling and artificial intelligence (AI). A leading expert in AI-driven data analysis, he has pioneered advanced algorithms for real-time forecasting and big data interpretation, significantly improving the understanding and management of environmental systems.

Dr. Bonakdari has authored four books, published over 320 peer-reviewed journal articles, contributed to more than 20 book chapters, and delivered over 100 presentations at national and international conferences. As a respected editorial board member of several leading journals, he continues to shape research in his field. His groundbreaking contributions have earned him global recognition, ranking him among the top 2% of the world's scientists from 2019 to 2024.

Sommaire:

1. Dataset Preparation 2. Pre-processing approaches 3. Post-processing approaches 4. Non-tuned single-layer feed-forward neural network Learning Machine ? Concept 5. Non-tuned single-layer feed-forward neural network Learning Machine ? Coding and implementation 6. Outlier-based models of the non-tuned neural network ? Concept 7. Outlier-based models of the non-tuned neural network ? Coding and implementation 8. Online Sequential non-tuned neural network ? Concept 9. Online Sequential non-tuned neural network ? Coding and implementation 10. Self-Adaptive Evolutionary of non-tuned neural network ? Concept 11. Self-Adaptive Evolutionary of non-tuned neural network ? Coding and implementation