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

Machine Learning in Earth, Environmental and Planetary Sciences provides a practical guide to implementing extreme learning machine algorithms in Earth and environmental data, with guided examples and MATLAB coding. It helps students, academics, and researchers understand how to apply machine learning to solve real-case problems and interpret the results.

Format: Paperback / softback
Length: 250 pages
Publication date: 01 July 2023
Publisher: Elsevier - Health Sciences Division

Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a comprehensive guide that delves into the implementation of diverse extreme learning machine algorithms for Earth and environmental data analysis. This invaluable resource offers step-by-step instructions, accompanied by real-world data examples, for numerous innovative and mathematically intricate machine learning techniques applicable to Earth, environmental, and planetary sciences. Each method is accompanied by detailed MATLAB coding, along with comprehensive explanations that highlight its advantages and limitations. Additionally, the book addresses essential postprocessing techniques necessary for accurate data interpretation. This book serves as a valuable resource for students, academics, and researchers seeking to gain a deep understanding of how machine learning algorithms can be applied to address real-world case problems, prepare data, and interpret the resulting insights. By exploring these topics in detail, readers will gain the skills and knowledge necessary to leverage machine learning in their research and contribute to the advancement of Earth, environmental, and planetary sciences.

Weight: 450g
Dimension: 276 x 215 (mm)
ISBN-13: 9780443152849