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Machine learning in earth, environmental and planetary sciences : theoretical and practical applications
- Author / Creator
- Available as
- Online
- Summary
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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...
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 dataProvides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codesIncludes numerous figures, illustrations and tables to help readers better understand the concepts covered
Details
- Creator
- Hossein Bonakdari, Isa Ebtehaj, and Joseph D. Ladouceur
- Format
- Books
- Language
- English
- Contributors
- Publication
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- Amsterdam, Netherlands : Elsevier, [2023]
- ©2023
- Physical Details
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- 1 online resource (579 pages)
- ISBNs
- 9780443152856, 0443152853, 9780443152849
- Includes bibliographical references and index.
- Intro -- Title page -- Table of Contents -- Copyright -- Dedication -- About the authors -- Preface -- Acknowledgments -- About the cover image -- Chapter 1. Dataset preparation -- Abstract -- 1.1 The modeling process -- 1.2 Data description -- 1.3 Different types of problems -- 1.4 Summary -- Appendix 1A Supporting information -- Appendix 1A Supplementary information -- References -- Chapter 2. Preprocessing approaches -- Abstract -- 2.1 Normalization -- 2.2 Standardization -- 2.3 Data splitting -- 2.4 Cross-validation -- 2.5 Summary -- Appendix 2A Supporting information -- Appendix 2A Supplementary information -- References -- Chapter 3. Postprocessing approaches -- Abstract -- 3.1 Introduction -- 3.2 Quantitative tools -- 3.3 Qualitative tools -- 3.4 Summary -- Appendix 3A Supporting information -- Appendix 3A Supplementary information -- References -- Chapter 4. Non-tuned single-layer feed-forward neural network learning machine-concept -- Abstract -- 4.1 Machine learning application in applied science -- 4.2 Mathematical definition of extreme learning machine model -- 4.3 Activation function in the extreme learning machine model -- 4.4 Summary -- References -- Chapter 5. Non-tuned single-layer feed-forward neural network learning machine-coding and implementation -- Abstract -- 5.1 Introduction -- 5.2 Extreme learning machine implementation in the MATLAB environment -- 5.3 Extreme learning machine modeling output -- 5.4 Calculator for extreme learning machine model -- 5.5 Effect of the extreme learning machine parameters -- 5.6 The effect of hidden layer neurons on Example 5 -- 5.7 Summary -- Appendix 5.A Supporting information -- Appendix 5.A Supporting information -- References -- Chapter 6. Outlier-based models of the non-tuned neural network-concept -- Abstract -- 6.1 Background of extreme learning machines
- 6.2 Extreme learning machine in the presence of outliers -- 6.3 Mathematical definition of extreme learning machine-based models -- 6.4 Summary -- References -- Chapter 7. Outlier-based models of the non-tuned neural network-coding and implementation -- Abstract -- 7.1 Developed extreme learning machine-based approaches in the presence of outliers -- 7.2 Implementation of the developed extreme learning machine-based models in the MATLAB -- 7.3 Calculator for outlier-based extreme learning machine models -- 7.4 Evaluating the effects of user-defined parameters on the modeling results of the extreme learning machine-based models -- 7.5 Summary -- Appendix 7.A Supporting information -- References -- Chapter 8. Online sequential non-tuned neural network-concept -- Abstract -- 8.1 Introduction -- 8.2 Main architectures of the single-layer feed-forward neural network -- 8.3 Development of the sequential-based learning algorithm -- 8.4 Main drawbacks of the classical sequential-based learning algorithms -- 8.5 Introduction to the online sequential extreme learning machine -- 8.6 Mathematical description of the online sequential extreme learning machine -- 8.7 Summary -- References -- Chapter 9. Online sequential nontuned neural network-coding and implementation -- Abstract -- 9.1 Summary of the online sequential extreme learning machine-based modeling algorithm -- 9.2 Implementation of the online sequential extreme learning machine in the MATLAB environment -- 9.3 Online sequential extreme learning machine calculator -- 9.4 Evaluating the effect of the online sequential extreme learning machine parameters on model performance -- 9.5 Summary -- Appendix 9.A Supporting information -- Appendix 9.A Supplementary information -- References -- Chapter 10. Self-adaptive evolutionary of non-tuned neural network-concept -- Abstract
- 10.1 Development process of single-layer feedforward neural network -- 10.2 Mathematical explanation of self-adaptive evolutionary extreme learning machine -- 10.3 Summary -- References -- Chapter 11. Self-adaptive evolutionary of non-tuned neural network-coding and implementation -- Abstract -- 11.1 Implementation of the self-adaptive evolutionary extreme learning machine in detail -- 11.2 Calculator for self-adaptive machine learning model -- 11.3 Sensitivity analysis of the self-adaptive machine learning model parameters -- 11.4 Summary -- Appendix 11A Supporting information -- Appendix 11.A Supplementary information -- References -- Index