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Elsevier

Machine Learning in Earth, Environmental and Planetary Sciences

Theoretical and Practical Applications

  • 1st Edition - June 27, 2023
  • Latest edition
  • Authors: Hossein Bonakdari, Isa Ebtehaj, Joseph Ladouceur
  • Language: English

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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Description

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.

Key features

  • 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

Readership

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

Table of contents

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

Product details

  • Edition: 1
  • Latest edition
  • Published: July 3, 2023
  • Language: English

About the authors

HB

Hossein Bonakdari

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.

Affiliations and expertise
Associate Professor, Department of Civil Engineering, University of Ottawa, Ontario, Canada

IE

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, Canada

JL

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, Canada

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