Handbook of HydroInformatics
eBook - ePub

Handbook of HydroInformatics

Volume I: Classic Soft-Computing Techniques

  1. 478 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Handbook of HydroInformatics

Volume I: Classic Soft-Computing Techniques

About this book

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.

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Yes, you can access Handbook of HydroInformatics by Saeid Eslamian,Faezeh Eslamian in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Environmental Management. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title of Book
  2. Cover image
  3. Title page
  4. Table of Contents
  5. Copyright
  6. Dedication
  7. Contributors
  8. About the editors
  9. Preface
  10. Chapter 1 Advanced machine learning techniques: Multivariate regression
  11. Chapter 2 Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity at the United States
  12. Chapter 3 Bayesian theory: Methods and applications
  13. Chapter 4 CFD models
  14. Chapter 5 Cross-validation
  15. Chapter 6 Comparative study on the selected node and link-based performance indices to investigate the hydraulic capacity of the water distribution network
  16. Chapter 7 The co-nodal system analysis
  17. Chapter 8 Data assimilation
  18. Chapter 9 Data reduction techniques
  19. Chapter 10 Decision tree algorithms
  20. Chapter 11 Entropy and resilience indices
  21. Chapter 12 Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models
  22. Chapter 13 Gene expression models
  23. Chapter 14 Gradient-based optimization
  24. Chapter 15 Gray wolf optimization algorithm
  25. Chapter 16 Kernel-based modeling
  26. Chapter 17 Large eddy simulation: Subgrid-scale modeling with neural network
  27. Chapter 18 Lattice Boltzmann method and its applications
  28. Chapter 19 Multigene genetic programming and its various applications
  29. Chapter 20 Ontology-based knowledge management framework in business organizations and water users networks in Tanzania
  30. Chapter 21 Parallel chaos search-based incremental extreme learning machine
  31. Chapter 22 Relevance vector machine (RVM)
  32. Chapter 23 Stochastic learning algorithms
  33. Chapter 24 Supporting vector machines
  34. Chapter 25 Uncertainty analysis using fuzzy models in hydroinformatics
  35. Chapter 26 Uncertainty-based resiliency evaluation
  36. Index