Handbook of HydroInformatics

Volume I: Classic Soft-Computing Techniques

Häftad, Engelska, 2022

AvSaeid Eslamian,Faezeh Eslamian,Iran) Eslamian, Saeid (Distinguished Full Professor, Disaster Relief: Resilient and Sustainable Water Resources, Isfahan University of Technology,Canada) Eslamian, Faezeh, Ph.D., CEP, (Project Manager, GHD, Quebec

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Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series.? Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo-Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc.?It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques.

This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering.

Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc.��
Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison.��
Introduces classic soft-computing techniques, necessary for a range of disciplines.

Produktinformation

Utgivningsdatum2022-12-05
Mått216 x 276 x undefined mm
Vikt1 310 g
FormatHäftad
SpråkEngelska
Antal sidor478
FörlagElsevier Science
ISBN9780128212851

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Miljöteknik inom Naturvetenskap och teknik

Saeid Eslamian received his PhD in Civil and Environmental Engineering from University of New South Wales, Australia in 1998. Saeid was Visiting Professor in Princeton University and ETH Zurich in 2005 and 2008 respectively. He has contributed to more than 1K publications in journals, conferences, books. Eslamian has been appointed as 2-Percent Top Researcher by Stanford University for several years. Currently, he is full professor of Hydrology and Water Resources and Director of Excellence Center in Risk Management and Natural Hazards. Isfahan University of Technology, His scientific interests are Floods, Droughts, Water Reuse, Climate Change Adaptation, Sustainability and ResilienceFaezeh Eslamian is a PhD holder of bioresource engineering from McGill University. Her research focuses on the development of a novel lime-based product to mitigate phosphorus loss from agricultural fields. Faezeh completed her bachelor’s and master’s degrees in civil and environmental engineering from Isfahan University of Technology, Iran, where she evaluated natural and low-cost absorb bents for the removal of pollutants such as textile dyes and heavy metals. Furthermore, she has conducted research on the worldwide water quality standards and wastewater reuse guidelines. Faezeh is an experienced multidisciplinary researcher with research interests in soil and water quality, environmental remediation, water reuse, and drought management.

1. Ant-Colony Based Simulation–Optimization Modeling2. Artificial Intelligent and Deep Learning3. Artificial Neural Network4. Bat Algorithm5. Citizen Science6. Conceptual Grey7. Data Reduction Techniques8. Data Science for Utilities and Urban Systems9. Decision Tree Algorithm10. Discrete Mixed Subdomain Least Squares11. Earthen Worm Algorithm12. Entropy and Resilience Indices13. Evolutionary Based Meta-Modeling14. Evolutionary Polynomial Regression Paradigm15. Firefly Algorithm16. Fish-Friendly Engineering (Fish Habitat Analysis)17. Fuzzy Logic18. Game Theory19. Genetic Algorithm20. Gene Expression Models21. Heuristic Burst Detection Method22. Honey-Bee Mating Optimization23. Hybrid Cuckoo–Harmony Search Algorithm24. Hybrid Mechanistic Data Driven Model25. Imperialist Competitive Algorithm26. Integrated Cellular Automata Evolution27. Lattice Boltzmann Method28. Meshless Particle Modeling29. Multivariare Regressions30. Ontology-Based Knowledge Management framework31. Random Forest32. Relevance Vector Machine33. Rhie and Chow Interpolation34. Supporting Vector Machine

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