Machine Learning in Earth, Environmental and Planetary Sciences - Hossein Bonakdari, Isa Ebtehaj, Joseph D. Ladouceur

Machine Learning in Earth, Environmental and Planetary Sciences

Theoretical and Practical Applications
Buch | Softcover
388 Seiten
2023
Elsevier - Health Sciences Division (Verlag)
978-0-443-15284-9 (ISBN)
157,10 inkl. MwSt
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.

Dr. Bonakdari obtained his PhD in Civil Engineering from the University of Caen Normandy (France). He has worked for several organizations and most recently as an Associate Professor at the Department of Civil Engineering of the 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 AI. 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. 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. 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.

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

Erscheinungsdatum
Verlagsort Philadelphia
Sprache englisch
Maße 216 x 276 mm
Gewicht 1070 g
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Naturwissenschaften Geowissenschaften Geologie
ISBN-10 0-443-15284-5 / 0443152845
ISBN-13 978-0-443-15284-9 / 9780443152849
Zustand Neuware
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