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.
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1. Ant-Colony Based Simulation–Optimization Modeling 2. Artificial Intelligent and Deep Learning 3. Artificial Neural Network 4. Bat Algorithm 5. Citizen Science 6. Conceptual Grey 7. Data Reduction Techniques 8. Data Science for Utilities and Urban Systems 9. Decision Tree Algorithm 10. Discrete Mixed Subdomain Least Squares 11. Earthen Worm Algorithm 12. Entropy and Resilience Indices 13. Evolutionary Based Meta-Modeling 14. Evolutionary Polynomial Regression Paradigm 15. Firefly Algorithm 16. Fish-Friendly Engineering (Fish Habitat Analysis) 17. Fuzzy Logic 18. Game Theory 19. Genetic Algorithm 20. Gene Expression Models 21. Heuristic Burst Detection Method 22. Honey-Bee Mating Optimization 23. Hybrid Cuckoo–Harmony Search Algorithm 24. Hybrid Mechanistic Data Driven Model 25. Imperialist Competitive Algorithm 26. Integrated Cellular Automata Evolution 27. Lattice Boltzmann Method 28. Meshless Particle Modeling 29. Multivariare Regressions 30. Ontology-Based Knowledge Management framework 31. Random Forest 32. Relevance Vector Machine 33. Rhie and Chow Interpolation 34. Supporting Vector Machine
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A fully comprehensive handbook that provides all the information needed to understand classic soft-computing techniques, machine learning, and more
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.
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Produktdetaljer

ISBN
9780128212851
Publisert
2022-12-05
Utgiver
Elsevier Science Publishing Co Inc; Elsevier Science Publishing Co Inc
Vekt
1310 gr
Høyde
276 mm
Bredde
216 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
478

Om bidragsyterne

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 Resilience Faezeh 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.