Machine Learning in Earth, Environmental and Planetary Sciences
Theoretical and Practical Applications

A practical guide to implementing machine learning and neural networks in Earth and Environmental science

Hossein Bonakdari (Author), Isa Ebtehaj (Author), Joseph D. Ladouceur (Author)

9780443152849

Paperback / softback, published 27 June 2023

388 pages
27.6 x 21.5 x 1.7 cm, 1.07 kg

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.

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

Subject Areas: Enterprise software [UFL], Earth sciences [RB]