Kooperativer Bibliotheksverbund

UID:

Umfang: 1 online resource (484 pages)

ISBN: 0-12-821970-X

Inhalt: 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.

Anmerkung: Intro -- Handbook of HydroInformatics: Volume I: Classic Soft-Computing Techniques -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Preface -- Chapter 1: Advanced machine learning techniques: Multivariate regression -- 1. Introduction -- 2. Linear regression -- 3. Multivariate linear regression -- 4. Gradient descent method -- 5. Polynomial regression -- 6. Overfitting and underfitting -- 7. Cross-validation -- 8. Comparison between linear and polynomial regressions -- 9. Learning curve -- 10. Regularized linear models -- 11. The ridge regression -- 12. The effect of collinearity in the coefficients of an estimator -- 13. Outliers impact -- 14. Lasso regression -- 15. Elastic net -- 16. Early stopping -- 17. Logistic regression -- 18. Estimation of probabilities -- 19. Training and the cost function -- 20. Conclusions -- Appendix: Python code -- Linear regression -- Gradient descent method -- Comparison between linear and polynomial regressions -- Learning curve -- The effect of collinearity in the coefficients of an estimator -- Outliers impact -- Lasso regression -- Elastic net -- Training and the cost function -- References -- Chapter 2: Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity ... -- 1. Introduction -- 2. Study area and data -- 3. Methodology -- 3.1. Feedforward artificial neural network -- 3.2. Dynamic evolving neural-fuzzy inference system -- 3.3. Bat algorithm optimized extreme learning machine -- 3.4. Multiple linear regression -- 3.5. Performance assessment of the models -- 4. Results and discussion -- 4.1. USGS 1497500 station -- 4.2. USGS 11501000 station -- 4.3. USGS 14210000 station -- 4.4. USGS 14211010 station -- 5. Conclusions -- References -- Chapter 3: Bayesian theory: Methods and applications -- 1. Introduction. , 2. Bayesian inference -- 3. Phases -- 4. Estimates -- 5. Theorem Bayes -- 5.1. Argument of Bayes -- 5.2. Bayesian estimation theory -- 5.3. Machine learning using Bayesian method -- 5.4. Bayesian theory in machine learning -- 5.5. Definition of basic concepts -- 5.6. Bayesian machine learning methods -- 5.7. Optimal Bayes classifier -- 5.7.1. Background and theory -- 5.8. Naive Bayes classifier -- 6. Bayesian network -- 7. History of Bayesian model application in water resources -- 8. Case study of Bayesian network application in modeling of evapotranspiration of reference plant -- 9. Conclusions -- References -- Chapter 4: CFD models -- 1. Introduction -- 2. Numerical model of one-dimensional advection dispersion equation (1D-ADE) -- 3. Physically influenced scheme -- 4. Finite Volume Solution of Saint-Venant equations for dam-break simulation using PIS -- 5. Discretization of continuity equation using PIS -- 6. Discretization of the momentum equation using PIS -- 7. Quasi-two-dimensional flow simulation -- 8. Numerical solution of quasi-two-dimensional model -- 9. 3D numerical modeling of flow in compound channel using turbulence models -- 10. Three-dimensional numerical model -- 11. Grid generation and the flow filed solution -- 12. Comparison of different turbulence models -- 13. Three-dimensional pollutant transfer modeling -- 14. Results of pollutant transfer modeling -- 15. Conclusions -- References -- Chapter 5: Cross-validation -- 1. Introduction -- 1.1. Importance of validation -- 1.2. Validation of the training process -- 2. Cross-validation -- 2.1. Exhaustive and nonexhaustive cross-validation -- 2.2. Repeated random subsampling cross-validation -- 2.3. Time-series cross-validation -- 2.4. k-fold cross-validation -- 2.5. Stratified k-fold cross-validation -- 2.6. Nested -- 3. Computational procedures -- 4. Conclusions -- References. , Chapter 6: Comparative study on the selected node and link-based performance indices to investigate the hydraulic capacit ... -- 1. Introduction -- 2. Resilience of water distribution network -- 3. Hydraulic uniformity index (HUI) -- 4. Mean excess pressure (MEP) -- 5. Proposed measure -- 5.1. Energy loss uniformity (ELU) -- 6. Hanoi network -- 7. Results and discussion -- 8. Conclusions -- References -- Chapter 7: The co-nodal system analysis -- 1. Introduction -- 2. Co-nodal and system analysis -- 3. Paleo-hydrology and remote sensing -- 4. Methods -- 5. Nodes and cyclic confluent system -- 5.1. H-cycloids analysis and fluvial dynamics -- 6. Three Danube phases -- 7. Danubian hypocycles as overlapping phases -- 8. Conclusions -- References -- Further reading -- Chapter 8: Data assimilation -- 1. Introduction -- 2. What is data assimilation? -- 3. Types of data assimilation methods -- 3.1. Types of updating procedure -- 3.1.1. Variational data assimilation -- 3.1.2. Sequential data assimilation -- 3.2. Types of updating variable -- 3.2.1. Updating input variable -- 3.2.2. Updating model parameter -- 3.2.3. Updating state variable -- 3.2.4. Updating output variable -- 4. Optimal filtering methods -- 4.1. Kalman filter -- 4.1.1. Kalman filter limitations -- 4.2. Transfer function -- 4.3. Extended Kalman filter -- 4.4. Unscented Kalman filter -- 5. Auto-regressive method -- 6. Considerations in using data assimilation -- 7. Conclusions -- References -- Chapter 9: Data reduction techniques -- 1. Introduction -- 2. Principal component analysis -- 3. Singular spectrum analysis -- 3.1. Univariate singular spectral analysis -- 3.2. Multivariate singular spectral analysis -- 4. Canonical correlation analysis -- 5. Factor analysis -- 5.1. Principal axis factoring -- 6. Random projection -- 7. Isometric mapping -- 8. Self-organizing maps. , 9. Discriminant analysis -- 10. Piecewise aggregate approximation -- 11. Clustering -- 11.1. k-means clustering -- 11.2. Hierarchical clustering -- 11.3. Density-based clustering -- 12. Conclusions -- References -- Chapter 10: Decision tree algorithms -- 1. Introduction -- 1.1. ID3 algorithm -- 1.2. C4.5 algorithm -- 1.3. CART algorithm -- 1.4. CHAID algorithm -- 1.5. M5 algorithm -- 1.6. Random forest -- 1.7. Application of DT algorithms in water sciences -- 2. M5 model tree -- 2.1. Splitting -- 2.2. Pruning -- 2.3. Smoothing -- 3. Data set -- 3.1. Empirical formula for flow discharge -- 3.2. Model evaluation and comparison -- 4. Modeling and results -- 4.1. Initial tree -- 4.2. Pruning -- 4.3. Comparing M5 model and empirical formula -- 5. Conclusions -- References -- Chapter 11: Entropy and resilience indices -- 1. Introduction -- 2. Water resource and infrastructure performance evaluation -- 3. Entropy -- 3.1. Thermodynamic entropy -- 3.2. Statistical-mechanical entropy -- 3.3. Information entropy -- 3.4. Application of entropy in water resources area -- 4. Resilience -- 4.1. Application of resilience in water resources area -- 4.2. Resilience in UWS -- 4.3. Resilience in urban environments -- 4.4. Resilience to floods -- 4.5. Resilience to drought -- 5. Conclusions -- References -- Chapter 12: Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models -- 1. Introduction -- 2. Methodology -- 2.1. Types of GARCH models -- 2.1.1. GARCH model -- 2.1.2. EGARCH model -- 2.1.3. GJR model -- 3. Application and results -- 4. Conclusions -- References -- Chapter 13: Gene expression models -- 1. Introduction -- 2. Genetic programming -- 2.1. The basic steps in GEP development -- 2.2. The basic steps in GEP development -- 3. Tree-based GEP -- 3.1. Tree depth control -- 3.2. Maximum tree depth -- 3.3. Penalizing the large trees. , 3.4. Dynamic maximum-depth technique -- 4. Linear genetic programming -- 5. Evolutionary polynomial regression -- 6. Multigene genetic programming -- 7. Pareto optimal-multigene genetic programming -- 8. Some applications of GEP-based models in hydro informatics -- 8.1. Derivation of quadric polynomial function using GEP -- 8.2. Derivation of Colebrook-White equation using GEP -- 8.3. Derivation of the exact form of shields diagram using GEP -- 8.4. Extraction of regime river equations using GEP -- 8.5. Extraction of longitudinal dispersion coefficient equations using GEP -- 9. Conclusions -- References -- Chapter 14: Gradient-based optimization -- 1. Introduction -- 2. Materials and method -- 2.1. GRG solver -- 3. Results and discussion -- 3.1. Solving nonlinear equations -- 3.2. Application in parameter estimation -- 3.3. Fitting empirical equations -- 4. Conclusions -- References -- Chapter 15: Gray wolf optimization algorithm -- 1. Introduction -- 2. Theory of GWO -- 3. Mathematical modeling of gray wolf optimizer -- 3.1. Social hierarchy -- 3.2. Encircling prey -- 3.3. Hunting behavior -- 3.4. Exploitation in GWO-attacking prey -- 3.5. Exploration in GWO-search for prey -- 4. Gray wolf optimization example for reservoir operation -- 5. Conclusions -- Appendix A: GWO Matlab codes for the reservoir example -- References -- Chapter 16: Kernel-based modeling -- 1. Introduction -- 2. Support vector machine -- 2.1. Support vector classification -- 2.1.1. Linear classifiers -- 2.1.2. Non-linear classifiers and kernels application -- 2.2. Support vector regression -- 3. Gaussian processes -- 3.1. Gaussian process regression -- 3.2. Gaussian process classification -- 4. Kernel extreme learning machine -- 5. Kernels type -- 5.1. Fisher kernel -- 5.2. Graph kernels -- 5.3. Kernel smoother -- 5.4. Polynomial kernel -- 5.5. Radial basis function kernel. , 5.6. Pearson kernel

Weitere Ausg.: Print version: Eslamian, Saeid Handbook of HydroInformatics San Diego : Elsevier,c2022 ISBN 9780128212851

Sprache: Englisch

UID:

Umfang: 1 online resource (484 pages)

ISBN: 0-12-821970-X

Inhalt: 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.

Anmerkung: Intro -- Handbook of HydroInformatics: Volume I: Classic Soft-Computing Techniques -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Preface -- Chapter 1: Advanced machine learning techniques: Multivariate regression -- 1. Introduction -- 2. Linear regression -- 3. Multivariate linear regression -- 4. Gradient descent method -- 5. Polynomial regression -- 6. Overfitting and underfitting -- 7. Cross-validation -- 8. Comparison between linear and polynomial regressions -- 9. Learning curve -- 10. Regularized linear models -- 11. The ridge regression -- 12. The effect of collinearity in the coefficients of an estimator -- 13. Outliers impact -- 14. Lasso regression -- 15. Elastic net -- 16. Early stopping -- 17. Logistic regression -- 18. Estimation of probabilities -- 19. Training and the cost function -- 20. Conclusions -- Appendix: Python code -- Linear regression -- Gradient descent method -- Comparison between linear and polynomial regressions -- Learning curve -- The effect of collinearity in the coefficients of an estimator -- Outliers impact -- Lasso regression -- Elastic net -- Training and the cost function -- References -- Chapter 2: Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity ... -- 1. Introduction -- 2. Study area and data -- 3. Methodology -- 3.1. Feedforward artificial neural network -- 3.2. Dynamic evolving neural-fuzzy inference system -- 3.3. Bat algorithm optimized extreme learning machine -- 3.4. Multiple linear regression -- 3.5. Performance assessment of the models -- 4. Results and discussion -- 4.1. USGS 1497500 station -- 4.2. USGS 11501000 station -- 4.3. USGS 14210000 station -- 4.4. USGS 14211010 station -- 5. Conclusions -- References -- Chapter 3: Bayesian theory: Methods and applications -- 1. Introduction. , 2. Bayesian inference -- 3. Phases -- 4. Estimates -- 5. Theorem Bayes -- 5.1. Argument of Bayes -- 5.2. Bayesian estimation theory -- 5.3. Machine learning using Bayesian method -- 5.4. Bayesian theory in machine learning -- 5.5. Definition of basic concepts -- 5.6. Bayesian machine learning methods -- 5.7. Optimal Bayes classifier -- 5.7.1. Background and theory -- 5.8. Naive Bayes classifier -- 6. Bayesian network -- 7. History of Bayesian model application in water resources -- 8. Case study of Bayesian network application in modeling of evapotranspiration of reference plant -- 9. Conclusions -- References -- Chapter 4: CFD models -- 1. Introduction -- 2. Numerical model of one-dimensional advection dispersion equation (1D-ADE) -- 3. Physically influenced scheme -- 4. Finite Volume Solution of Saint-Venant equations for dam-break simulation using PIS -- 5. Discretization of continuity equation using PIS -- 6. Discretization of the momentum equation using PIS -- 7. Quasi-two-dimensional flow simulation -- 8. Numerical solution of quasi-two-dimensional model -- 9. 3D numerical modeling of flow in compound channel using turbulence models -- 10. Three-dimensional numerical model -- 11. Grid generation and the flow filed solution -- 12. Comparison of different turbulence models -- 13. Three-dimensional pollutant transfer modeling -- 14. Results of pollutant transfer modeling -- 15. Conclusions -- References -- Chapter 5: Cross-validation -- 1. Introduction -- 1.1. Importance of validation -- 1.2. Validation of the training process -- 2. Cross-validation -- 2.1. Exhaustive and nonexhaustive cross-validation -- 2.2. Repeated random subsampling cross-validation -- 2.3. Time-series cross-validation -- 2.4. k-fold cross-validation -- 2.5. Stratified k-fold cross-validation -- 2.6. Nested -- 3. Computational procedures -- 4. Conclusions -- References. , Chapter 6: Comparative study on the selected node and link-based performance indices to investigate the hydraulic capacit ... -- 1. Introduction -- 2. Resilience of water distribution network -- 3. Hydraulic uniformity index (HUI) -- 4. Mean excess pressure (MEP) -- 5. Proposed measure -- 5.1. Energy loss uniformity (ELU) -- 6. Hanoi network -- 7. Results and discussion -- 8. Conclusions -- References -- Chapter 7: The co-nodal system analysis -- 1. Introduction -- 2. Co-nodal and system analysis -- 3. Paleo-hydrology and remote sensing -- 4. Methods -- 5. Nodes and cyclic confluent system -- 5.1. H-cycloids analysis and fluvial dynamics -- 6. Three Danube phases -- 7. Danubian hypocycles as overlapping phases -- 8. Conclusions -- References -- Further reading -- Chapter 8: Data assimilation -- 1. Introduction -- 2. What is data assimilation? -- 3. Types of data assimilation methods -- 3.1. Types of updating procedure -- 3.1.1. Variational data assimilation -- 3.1.2. Sequential data assimilation -- 3.2. Types of updating variable -- 3.2.1. Updating input variable -- 3.2.2. Updating model parameter -- 3.2.3. Updating state variable -- 3.2.4. Updating output variable -- 4. Optimal filtering methods -- 4.1. Kalman filter -- 4.1.1. Kalman filter limitations -- 4.2. Transfer function -- 4.3. Extended Kalman filter -- 4.4. Unscented Kalman filter -- 5. Auto-regressive method -- 6. Considerations in using data assimilation -- 7. Conclusions -- References -- Chapter 9: Data reduction techniques -- 1. Introduction -- 2. Principal component analysis -- 3. Singular spectrum analysis -- 3.1. Univariate singular spectral analysis -- 3.2. Multivariate singular spectral analysis -- 4. Canonical correlation analysis -- 5. Factor analysis -- 5.1. Principal axis factoring -- 6. Random projection -- 7. Isometric mapping -- 8. Self-organizing maps. , 9. Discriminant analysis -- 10. Piecewise aggregate approximation -- 11. Clustering -- 11.1. k-means clustering -- 11.2. Hierarchical clustering -- 11.3. Density-based clustering -- 12. Conclusions -- References -- Chapter 10: Decision tree algorithms -- 1. Introduction -- 1.1. ID3 algorithm -- 1.2. C4.5 algorithm -- 1.3. CART algorithm -- 1.4. CHAID algorithm -- 1.5. M5 algorithm -- 1.6. Random forest -- 1.7. Application of DT algorithms in water sciences -- 2. M5 model tree -- 2.1. Splitting -- 2.2. Pruning -- 2.3. Smoothing -- 3. Data set -- 3.1. Empirical formula for flow discharge -- 3.2. Model evaluation and comparison -- 4. Modeling and results -- 4.1. Initial tree -- 4.2. Pruning -- 4.3. Comparing M5 model and empirical formula -- 5. Conclusions -- References -- Chapter 11: Entropy and resilience indices -- 1. Introduction -- 2. Water resource and infrastructure performance evaluation -- 3. Entropy -- 3.1. Thermodynamic entropy -- 3.2. Statistical-mechanical entropy -- 3.3. Information entropy -- 3.4. Application of entropy in water resources area -- 4. Resilience -- 4.1. Application of resilience in water resources area -- 4.2. Resilience in UWS -- 4.3. Resilience in urban environments -- 4.4. Resilience to floods -- 4.5. Resilience to drought -- 5. Conclusions -- References -- Chapter 12: Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models -- 1. Introduction -- 2. Methodology -- 2.1. Types of GARCH models -- 2.1.1. GARCH model -- 2.1.2. EGARCH model -- 2.1.3. GJR model -- 3. Application and results -- 4. Conclusions -- References -- Chapter 13: Gene expression models -- 1. Introduction -- 2. Genetic programming -- 2.1. The basic steps in GEP development -- 2.2. The basic steps in GEP development -- 3. Tree-based GEP -- 3.1. Tree depth control -- 3.2. Maximum tree depth -- 3.3. Penalizing the large trees. , 3.4. Dynamic maximum-depth technique -- 4. Linear genetic programming -- 5. Evolutionary polynomial regression -- 6. Multigene genetic programming -- 7. Pareto optimal-multigene genetic programming -- 8. Some applications of GEP-based models in hydro informatics -- 8.1. Derivation of quadric polynomial function using GEP -- 8.2. Derivation of Colebrook-White equation using GEP -- 8.3. Derivation of the exact form of shields diagram using GEP -- 8.4. Extraction of regime river equations using GEP -- 8.5. Extraction of longitudinal dispersion coefficient equations using GEP -- 9. Conclusions -- References -- Chapter 14: Gradient-based optimization -- 1. Introduction -- 2. Materials and method -- 2.1. GRG solver -- 3. Results and discussion -- 3.1. Solving nonlinear equations -- 3.2. Application in parameter estimation -- 3.3. Fitting empirical equations -- 4. Conclusions -- References -- Chapter 15: Gray wolf optimization algorithm -- 1. Introduction -- 2. Theory of GWO -- 3. Mathematical modeling of gray wolf optimizer -- 3.1. Social hierarchy -- 3.2. Encircling prey -- 3.3. Hunting behavior -- 3.4. Exploitation in GWO-attacking prey -- 3.5. Exploration in GWO-search for prey -- 4. Gray wolf optimization example for reservoir operation -- 5. Conclusions -- Appendix A: GWO Matlab codes for the reservoir example -- References -- Chapter 16: Kernel-based modeling -- 1. Introduction -- 2. Support vector machine -- 2.1. Support vector classification -- 2.1.1. Linear classifiers -- 2.1.2. Non-linear classifiers and kernels application -- 2.2. Support vector regression -- 3. Gaussian processes -- 3.1. Gaussian process regression -- 3.2. Gaussian process classification -- 4. Kernel extreme learning machine -- 5. Kernels type -- 5.1. Fisher kernel -- 5.2. Graph kernels -- 5.3. Kernel smoother -- 5.4. Polynomial kernel -- 5.5. Radial basis function kernel. , 5.6. Pearson kernel

Weitere Ausg.: Print version: Eslamian, Saeid Handbook of HydroInformatics San Diego : Elsevier,c2022 ISBN 9780128212851

Sprache: Englisch

UID:

Umfang: 1 online resource (484 pages)

ISBN: 0-12-821970-X

Inhalt: 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.

Anmerkung: Intro -- Handbook of HydroInformatics: Volume I: Classic Soft-Computing Techniques -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Preface -- Chapter 1: Advanced machine learning techniques: Multivariate regression -- 1. Introduction -- 2. Linear regression -- 3. Multivariate linear regression -- 4. Gradient descent method -- 5. Polynomial regression -- 6. Overfitting and underfitting -- 7. Cross-validation -- 8. Comparison between linear and polynomial regressions -- 9. Learning curve -- 10. Regularized linear models -- 11. The ridge regression -- 12. The effect of collinearity in the coefficients of an estimator -- 13. Outliers impact -- 14. Lasso regression -- 15. Elastic net -- 16. Early stopping -- 17. Logistic regression -- 18. Estimation of probabilities -- 19. Training and the cost function -- 20. Conclusions -- Appendix: Python code -- Linear regression -- Gradient descent method -- Comparison between linear and polynomial regressions -- Learning curve -- The effect of collinearity in the coefficients of an estimator -- Outliers impact -- Lasso regression -- Elastic net -- Training and the cost function -- References -- Chapter 2: Bat algorithm optimized extreme learning machine: A new modeling strategy for predicting river water turbidity ... -- 1. Introduction -- 2. Study area and data -- 3. Methodology -- 3.1. Feedforward artificial neural network -- 3.2. Dynamic evolving neural-fuzzy inference system -- 3.3. Bat algorithm optimized extreme learning machine -- 3.4. Multiple linear regression -- 3.5. Performance assessment of the models -- 4. Results and discussion -- 4.1. USGS 1497500 station -- 4.2. USGS 11501000 station -- 4.3. USGS 14210000 station -- 4.4. USGS 14211010 station -- 5. Conclusions -- References -- Chapter 3: Bayesian theory: Methods and applications -- 1. Introduction. , 2. Bayesian inference -- 3. Phases -- 4. Estimates -- 5. Theorem Bayes -- 5.1. Argument of Bayes -- 5.2. Bayesian estimation theory -- 5.3. Machine learning using Bayesian method -- 5.4. Bayesian theory in machine learning -- 5.5. Definition of basic concepts -- 5.6. Bayesian machine learning methods -- 5.7. Optimal Bayes classifier -- 5.7.1. Background and theory -- 5.8. Naive Bayes classifier -- 6. Bayesian network -- 7. History of Bayesian model application in water resources -- 8. Case study of Bayesian network application in modeling of evapotranspiration of reference plant -- 9. Conclusions -- References -- Chapter 4: CFD models -- 1. Introduction -- 2. Numerical model of one-dimensional advection dispersion equation (1D-ADE) -- 3. Physically influenced scheme -- 4. Finite Volume Solution of Saint-Venant equations for dam-break simulation using PIS -- 5. Discretization of continuity equation using PIS -- 6. Discretization of the momentum equation using PIS -- 7. Quasi-two-dimensional flow simulation -- 8. Numerical solution of quasi-two-dimensional model -- 9. 3D numerical modeling of flow in compound channel using turbulence models -- 10. Three-dimensional numerical model -- 11. Grid generation and the flow filed solution -- 12. Comparison of different turbulence models -- 13. Three-dimensional pollutant transfer modeling -- 14. Results of pollutant transfer modeling -- 15. Conclusions -- References -- Chapter 5: Cross-validation -- 1. Introduction -- 1.1. Importance of validation -- 1.2. Validation of the training process -- 2. Cross-validation -- 2.1. Exhaustive and nonexhaustive cross-validation -- 2.2. Repeated random subsampling cross-validation -- 2.3. Time-series cross-validation -- 2.4. k-fold cross-validation -- 2.5. Stratified k-fold cross-validation -- 2.6. Nested -- 3. Computational procedures -- 4. Conclusions -- References. , Chapter 6: Comparative study on the selected node and link-based performance indices to investigate the hydraulic capacit ... -- 1. Introduction -- 2. Resilience of water distribution network -- 3. Hydraulic uniformity index (HUI) -- 4. Mean excess pressure (MEP) -- 5. Proposed measure -- 5.1. Energy loss uniformity (ELU) -- 6. Hanoi network -- 7. Results and discussion -- 8. Conclusions -- References -- Chapter 7: The co-nodal system analysis -- 1. Introduction -- 2. Co-nodal and system analysis -- 3. Paleo-hydrology and remote sensing -- 4. Methods -- 5. Nodes and cyclic confluent system -- 5.1. H-cycloids analysis and fluvial dynamics -- 6. Three Danube phases -- 7. Danubian hypocycles as overlapping phases -- 8. Conclusions -- References -- Further reading -- Chapter 8: Data assimilation -- 1. Introduction -- 2. What is data assimilation? -- 3. Types of data assimilation methods -- 3.1. Types of updating procedure -- 3.1.1. Variational data assimilation -- 3.1.2. Sequential data assimilation -- 3.2. Types of updating variable -- 3.2.1. Updating input variable -- 3.2.2. Updating model parameter -- 3.2.3. Updating state variable -- 3.2.4. Updating output variable -- 4. Optimal filtering methods -- 4.1. Kalman filter -- 4.1.1. Kalman filter limitations -- 4.2. Transfer function -- 4.3. Extended Kalman filter -- 4.4. Unscented Kalman filter -- 5. Auto-regressive method -- 6. Considerations in using data assimilation -- 7. Conclusions -- References -- Chapter 9: Data reduction techniques -- 1. Introduction -- 2. Principal component analysis -- 3. Singular spectrum analysis -- 3.1. Univariate singular spectral analysis -- 3.2. Multivariate singular spectral analysis -- 4. Canonical correlation analysis -- 5. Factor analysis -- 5.1. Principal axis factoring -- 6. Random projection -- 7. Isometric mapping -- 8. Self-organizing maps. , 9. Discriminant analysis -- 10. Piecewise aggregate approximation -- 11. Clustering -- 11.1. k-means clustering -- 11.2. Hierarchical clustering -- 11.3. Density-based clustering -- 12. Conclusions -- References -- Chapter 10: Decision tree algorithms -- 1. Introduction -- 1.1. ID3 algorithm -- 1.2. C4.5 algorithm -- 1.3. CART algorithm -- 1.4. CHAID algorithm -- 1.5. M5 algorithm -- 1.6. Random forest -- 1.7. Application of DT algorithms in water sciences -- 2. M5 model tree -- 2.1. Splitting -- 2.2. Pruning -- 2.3. Smoothing -- 3. Data set -- 3.1. Empirical formula for flow discharge -- 3.2. Model evaluation and comparison -- 4. Modeling and results -- 4.1. Initial tree -- 4.2. Pruning -- 4.3. Comparing M5 model and empirical formula -- 5. Conclusions -- References -- Chapter 11: Entropy and resilience indices -- 1. Introduction -- 2. Water resource and infrastructure performance evaluation -- 3. Entropy -- 3.1. Thermodynamic entropy -- 3.2. Statistical-mechanical entropy -- 3.3. Information entropy -- 3.4. Application of entropy in water resources area -- 4. Resilience -- 4.1. Application of resilience in water resources area -- 4.2. Resilience in UWS -- 4.3. Resilience in urban environments -- 4.4. Resilience to floods -- 4.5. Resilience to drought -- 5. Conclusions -- References -- Chapter 12: Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models -- 1. Introduction -- 2. Methodology -- 2.1. Types of GARCH models -- 2.1.1. GARCH model -- 2.1.2. EGARCH model -- 2.1.3. GJR model -- 3. Application and results -- 4. Conclusions -- References -- Chapter 13: Gene expression models -- 1. Introduction -- 2. Genetic programming -- 2.1. The basic steps in GEP development -- 2.2. The basic steps in GEP development -- 3. Tree-based GEP -- 3.1. Tree depth control -- 3.2. Maximum tree depth -- 3.3. Penalizing the large trees. , 3.4. Dynamic maximum-depth technique -- 4. Linear genetic programming -- 5. Evolutionary polynomial regression -- 6. Multigene genetic programming -- 7. Pareto optimal-multigene genetic programming -- 8. Some applications of GEP-based models in hydro informatics -- 8.1. Derivation of quadric polynomial function using GEP -- 8.2. Derivation of Colebrook-White equation using GEP -- 8.3. Derivation of the exact form of shields diagram using GEP -- 8.4. Extraction of regime river equations using GEP -- 8.5. Extraction of longitudinal dispersion coefficient equations using GEP -- 9. Conclusions -- References -- Chapter 14: Gradient-based optimization -- 1. Introduction -- 2. Materials and method -- 2.1. GRG solver -- 3. Results and discussion -- 3.1. Solving nonlinear equations -- 3.2. Application in parameter estimation -- 3.3. Fitting empirical equations -- 4. Conclusions -- References -- Chapter 15: Gray wolf optimization algorithm -- 1. Introduction -- 2. Theory of GWO -- 3. Mathematical modeling of gray wolf optimizer -- 3.1. Social hierarchy -- 3.2. Encircling prey -- 3.3. Hunting behavior -- 3.4. Exploitation in GWO-attacking prey -- 3.5. Exploration in GWO-search for prey -- 4. Gray wolf optimization example for reservoir operation -- 5. Conclusions -- Appendix A: GWO Matlab codes for the reservoir example -- References -- Chapter 16: Kernel-based modeling -- 1. Introduction -- 2. Support vector machine -- 2.1. Support vector classification -- 2.1.1. Linear classifiers -- 2.1.2. Non-linear classifiers and kernels application -- 2.2. Support vector regression -- 3. Gaussian processes -- 3.1. Gaussian process regression -- 3.2. Gaussian process classification -- 4. Kernel extreme learning machine -- 5. Kernels type -- 5.1. Fisher kernel -- 5.2. Graph kernels -- 5.3. Kernel smoother -- 5.4. Polynomial kernel -- 5.5. Radial basis function kernel. , 5.6. Pearson kernel

Weitere Ausg.: Print version: Eslamian, Saeid Handbook of HydroInformatics San Diego : Elsevier,c2022 ISBN 9780128212851

Sprache: Englisch