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Machine Learning in Earth, Environmental and Planetary Sciences
Theoretical and Practical Applications
- 1st Edition - June 27, 2023
- Authors: Hossein Bonakdari, Isa Ebtehaj, Joseph Ladouceur
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 2 8 4 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 2 8 5 - 6
Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning… Read more
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Request a sales quoteMachine 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
Researchers, professionals, academics, graduate students, and upper-level undergraduates in Earth sciences, environmental and planetary sciences, Researchers in neighboring fields such as applied sciences who are involved in machine learning or artificial intelligence applications for theoretical and practical studies can use the detailed guides to apply these techniques to their own datasets
- Cover image
- 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
- No. of pages: 388
- Language: English
- Edition: 1
- Published: June 27, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780443152849
- eBook ISBN: 9780443152856
HB
Hossein Bonakdari
Prof. Hossein Bonakdari obtained his PhD in civil engineering from the University of Caen Normandy, France. He has worked for several organizations, most recently as Professor at the Department of Civil Engineering, University of Ottawa, Canada. He is one of the most influential scientists in the field of developing novel algorithms for solving practical problems through the decision-making abilities of artificial intelligence. His research also focuses on creating comprehensive methodologies in the areas of simulation modeling, optimization, and machine learning algorithms. The results obtained from his research have been published in international journals and presented at international conferences. He was included in the list of the world’s top 2% scientists, published by Stanford University, and is on the editorial board of several journals.
Affiliations and expertise
Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Ottawa, Ottawa, Ontario, CanadaIE
Isa Ebtehaj
Isa Ebtehaj is a PhD student in the Soil and Environment department at the Faculty of Agriculture and Food Science, Laval University, Canada. He obtained his MSc degree from Razi University in 2014 and his MSc thesis was nominated as the best thesis by the Iranian Water and Wastewater Engineering Association, the Iranian Hydraulic Association, and the Vice-Presidency for Science and Technology (Iran). His fields of specialization and interest include machine learning, development of hybrid model methods, evolutionary optimization, hydrological time series, and sediment transport. From 2014 to 2019, he participated as a researcher in several industrial research projects through the Water and Wastewater Research Center and the Environmental Research Center, Razi University. Results obtained from his research studies have been published in more than 100 papers in international journals. He also has more than 30 presentations at national and international conferences and has published eight book chapters.
Affiliations and expertise
PhD Student, Soil and Environment Department, Faculty of Agriculture and Food Science, Laval University, CanadaJL
Joseph Ladouceur
Joseph Ladouceur is a current PhD Student at the University of Ottawa. Having completed his BASc in civil engineering at the University of Ottawa with Summa Cum Laude distinction, Joseph fast-tracked to the PhD program under the supervision of Dr. Roberto Narbaitz and Dr. Christopher Lan. A previous NSERC CGS-M award holder, his research interests are focused on pre-treatment strategies for drinking water. Prior to commencing his doctoral studies, Joseph worked as a senior inspector at McIntosh Perry Consulting Engineers Ltd. performing services on provincial bridge infrastructure projects. Joseph is a registered EIT with Professional Engineers Ontario and a member of the American Water Works Association.
Affiliations and expertise
PhD Student, Department of Civil Engineering, Faculty of Engineering, University of Ottawa, CanadaRead Machine Learning in Earth, Environmental and Planetary Sciences on ScienceDirect