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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Table of Contents
1. Ant-Colony Based Simulation-Optimization Modeling2. 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
Authors
Saeid Eslamian Distinguished Full Professor, Disaster Relief: Resilient and Sustainable Water Resources, Isfahan University of Technology, Iran.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 CEP, Project Manager at GHD, Quebec, Canada. 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.
