Machine Learning in Earth, Environmental and Planetary Sciences
Elsevier - Health Sciences Division (Verlag)
978-0-443-15284-9 (ISBN)
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.
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. 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 | 10.07.2023 |
|---|---|
| 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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