Water Engineering Modeling and Mathematic Tools

Erscheinungsjahr 2021
ISBN: 978-0-12-820644-7
Verlag: William Andrew Publishing

Buch, Englisch, 590 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 880 g

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.

This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.

Zielgruppe

<p>Water Engineers, water and civil engineering practitioners, hydrologists and policy-makers</p>

Autoren/Hrsg.

Samui, Pijush

Bonakdari, Hossein

Deo, Ravinesh

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Wasserbau
• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Wasserversorgung, Wasseraufbereitung
• Geowissenschaften Geologie Hydrologie, Hydrogeologie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie

Inhaltsverzeichnis

1. Numerical insight into the sonolytic ozonation applied for water treatment 2. Sea water desalination 3. New Formulation for Predicting Soil Moisture Content using only Soil Temperature as Predictor: Multivariate Adaptive Regression Splines Vs Random Forest, MLPNN, M5Tree and MLR 4. Numerical simulation of acoustic cavitation and its chemical effect in seawater: Toward understanding the multiple role of salinity in the sonochemical degradation of organic pollutants 5. Computer simulation of N2O/argon gas mixture effect on the acoustic generation of hydroxyl radicals in water: Toward understanding the mechanism of N2O inhibited/improved-sonochemical processes 6. Recent remediation technologies for contaminated water 7. Water chemistry in the biological studies by using nuclear analytical techniques 8. Protection from harmful effects of water - Examples from Serbia 9. Gravity-driven Membrane Filtration for Water and Wastewater Treatment 10. Modelling Hydraulics and Water Quality in Distribution Networks: Review of Existing Mathematical Techniques and Software 11. Remediation of oil contaminated water for reuse using polymeric nanocomposites 12. Hydrological contaminant transport 13. Desalination technologies and potential mathematical modelling for sustainable water-energy nexus 14. Emerging Trends of Water Quality Monitoring and Applications of Multivariate Tools 15. Experimental study on bed deformations due to flows over macro-roughness conditions 16. An Introduction to Hydraulics and Hydraulic Structures 17. Mixing of Inclined Dense Jets: A Numerical Modelling 18. Real-time Flood Forecasting with Weather Radar and Distributed Hydrological Model 19. Flood susceptibility mapping in ungauged watersheds using a statistical model 20. Groundwater potential mapping using hybridization of simulated annealing and random forest 21. Synergy of Combining Megahertz Ultrasound Frequency and Heat Activated Persulfate for Wastewater Decontamination: Micro-modeling of Acoustic Cavitation and its Role in the Sono-hybrid Process 22. On the sonochemical production of nitrite and nitrate in water: Computational study 23. Numerical insight into the liquid compressibility effect on the sonochemical activity of acoustic bubbles 24. Extremely Randomized Tree: A New Machines Learning Method for predicting Coagulant Dosage in Drinking Water Treatment Plant 25. Pareto Design of Multi-Objective Evolutionary Neuro-Fuzzy System for Predicting Scour Depth around Bridge Piers 26. River Flow Forecasting Using Stochastic and Neuro-Fuzzy Embedded Technique: A Comprehensive Pre-Processing Based Assessment 27. Desalination technologies and potential mathematical modelling for sustainable water-energy nexus

Über Autor(innen)

Deo, Ravinesh
Professor Ravinesh Deo is an Associate Professor at University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence. He also serves as Associate Editor for two international journals: Stochastic Environmental Research and Risk Assessment and the ASCE Journal Hydrologic Engineering journal (USA). As an Applied Data Scientist with proven leadership in artificial intelligence, his research develops decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems especially using deep learning explainable AI, convolutional neural networks and long short-term memory networks. He was awarded internationally competitive fellowships including Queensland Government U.S. Smithsonian Fellowship, Australia-India Strategic Fellowship, Australia-China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and Endeavour Fellowship. He is a member of scientific bodies, won Publication Excellence Awards, Head of Department Research Award, Dean's Commendation for Postgraduate Supervision, BSc Gold Medal for Academic Excellence and he was the Dux of Fiji in Year 13 examinations. Professor Deo held visiting positions at United States Tropical Research Institute, Chinese Academy of Science, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University and National University of Singapore. He has undertaken knowledge exchange programs in Singapore, Japan, Europe, China, USA and Canada and secured international standing by researching innovative problems with global researchers. He has published Books with Springer Nature, Elsevier and IGI and over 190 publications of which over 140 are Q1 including refereed conferences, Edited Books and book chapters. Professor Deo's papers have been cited over 4,000 times with Google Scholar H-Index of 36 and a Field Weighted Citation Index exceeding 3.5.

Samui, Pijush
Dr. Samui is an Associate Professor in the Department of Civil Engineering at NIT Patna, India. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore, India, in 2008. His research interests include geohazard, earthquake engineering, concrete technology, pile foundation and slope stability, and application of AI for solving different problems in civil engineering. Dr. Samui is a repeat Elsevier editor but also a prolific contributor to journal papers, book chapters, and peer-reviewed conference proceedings.

Bonakdari, Hossein
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.