Water Engineering Modeling and Mathematic Tools
View on ScienceDirect

Water Engineering Modeling and Mathematic Tools

1st Edition - February 5, 2021

Write a review

  • Editors: Pijush Samui, Hossein Bonakdari, Ravinesh Deo
  • eBook ISBN: 9780128208779
  • Paperback ISBN: 9780128206447

Purchase options

Purchase options
DRM-free (Mobi, EPub, PDF)
eBook Format Help
Available
Sales tax will be calculated at check-out

Institutional Subscription

Support CenterReturns & Refunds
Free Global Shipping
No minimum order

Description

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.

Key Features

  • Includes firsthand experience about artificial intelligence models, utilizing case studies
  • Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow
  • Presents the application of new instruments in water engineering

Readership

Water Engineers, water and civil engineering practitioners, hydrologists and policy-makers

Table of Contents

  • 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

Product details

  • No. of pages: 590
  • Language: English
  • Copyright: © Elsevier 2021
  • Published: February 5, 2021
  • Imprint: Elsevier
  • eBook ISBN: 9780128208779
  • Paperback ISBN: 9780128206447

About the Editors

Pijush Samui

Dr. Pijush Samui is an Associate Professor, in the Department of Civil Engineering, at NIT Patna, India, and an Adjunct Professor at Ton Duc Thang University in Ho Chi Minh City, Vietnam. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore in 2008. His research interests include geohazards; earthquake geotechnical engineering; concrete technology, pile foundation and slope stability and application of AI in civil engineering. He has published more than 21 books, 32 book chapters and over 200 research papers in high impact factor journals as well as 30 conference proceedings.

Affiliations and Expertise

Associate Professor, National Institute of Technology, Patna, India

Hossein Bonakdari

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.

Affiliations and Expertise

Associate Professor, Dept. of Civil Engineering, Faculty of Engineering, University of Ottawa, Ottawa, Ontario, Canada

Ravinesh Deo

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.

Affiliations and Expertise

Associate Professor, University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence

Ratings and Reviews

Write a review

Latest reviews

(Total rating for all reviews)

  • P S. Sun Jun 27 2021

    Very informative, recommended

    The book covers in-depth knowledge about various techniques, data-driven models, and experimental techniques, which can be helpful to deal with water engineering-related issues. Overall, the book can be recommended to Civil and environmental engineers, Govt. and industrial stakeholders, as well as management experts, to deal with water engineering issues more effectively.

  • Avijit B. Sat Jun 19 2021

    Reviews on Water Engineering Modeling and Mathematic Tools

    The book is presented machine learing and AI application in water engineering which is quite impressive. It contains some advanced instrument application in water engineering.