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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xv 8. About the editors - p. xvii 9. Preface - p. xviii 10. Chapter 1. Advanced machine learning techniques: Multivariate regression - p. 1 11. Chapter 2. Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity at the United States - p. 39 12. Chapter 3. Bayesian theory: Methods and applications - p. 57 13. Chapter 4. CFD models - p. 69 14. Chapter 5. Cross-validation - p. 89 15. Chapter 6. Comparative study on the selected node and link-based performance indices to investigate the hydraulic capacity of the water distribution network - p. 107 16. Chapter 7. The co-nodal system analysis - p. 119 17. Chapter 8. Data assimilation - p. 135 18. Chapter 9. Data reduction techniques - p. 153 19. Chapter 10. Decision tree algorithms - p. 171 20. Chapter 11. Entropy and resilience indices - p. 189 21. Chapter 12. Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models - p. 207 22. Chapter 13. Gene expression models - p. 221 23. Chapter 14. Gradient-based optimization - p. 243 24. Chapter 15. Gray wolf optimization algorithm - p. 253 25. Chapter 16. Kernel-based modeling - p. 267 26. Chapter 17. Large eddy simulation: Subgrid-scale modeling with neural network - p. 283 27. Chapter 18. Lattice Boltzmann method and its applications - p. 290 28. Chapter 19. Multigene genetic programming and its various applications - p. 321 29. Chapter 20. Ontology-based knowledge management framework in business organizations and water users networks in Tanzania - p. 333 30. Chapter 21. Parallel chaos search-based incremental extreme learning machine - p. 349 31. Chapter 22. Relevance vector machine (RVM) - p. 365 32. Chapter 23. Stochastic learning algorithms - p. 385 33. Chapter 24. Supporting vector machines - p. 411 34. Chapter 25. Uncertainty analysis using fuzzy models in hydroinformatics - p. 423 35. Chapter 26. Uncertainty-based resiliency evaluation - p. 435 36. Index - p. 455
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xv 8. About the editors - p. xvii 9. Preface - p. xviii 10. Chapter 1. Advanced machine learning techniques: Multivariate regression - p. 1 11. Chapter 2. Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity at the United States - p. 39 12. Chapter 3. Bayesian theory: Methods and applications - p. 57 13. Chapter 4. CFD models - p. 69 14. Chapter 5. Cross-validation - p. 89 15. Chapter 6. Comparative study on the selected node and link-based performance indices to investigate the hydraulic capacity of the water distribution network - p. 107 16. Chapter 7. The co-nodal system analysis - p. 119 17. Chapter 8. Data assimilation - p. 135 18. Chapter 9. Data reduction techniques - p. 153 19. Chapter 10. Decision tree algorithms - p. 171 20. Chapter 11. Entropy and resilience indices - p. 189 21. Chapter 12. Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models - p. 207 22. Chapter 13. Gene expression models - p. 221 23. Chapter 14. Gradient-based optimization - p. 243 24. Chapter 15. Gray wolf optimization algorithm - p. 253 25. Chapter 16. Kernel-based modeling - p. 267 26. Chapter 17. Large eddy simulation: Subgrid-scale modeling with neural network - p. 283 27. Chapter 18. Lattice Boltzmann method and its applications - p. 290 28. Chapter 19. Multigene genetic programming and its various applications - p. 321 29. Chapter 20. Ontology-based knowledge management framework in business organizations and water users networks in Tanzania - p. 333 30. Chapter 21. Parallel chaos search-based incremental extreme learning machine - p. 349 31. Chapter 22. Relevance vector machine (RVM) - p. 365 32. Chapter 23. Stochastic learning algorithms - p. 385 33. Chapter 24. Supporting vector machines - p. 411 34. Chapter 25. Uncertainty analysis using fuzzy models in hydroinformatics - p. 423 35. Chapter 26. Uncertainty-based resiliency evaluation - p. 435 36. Index - p. 455
