Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode.
This is an interdisciplinary book, 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, 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. To Late George Edward Pelham Box (British Statistician: 1919–2013) - p. iii 7. Contributors - p. xiii 8. About the Editors - p. xvii 9. Preface - p. xviii 10. Chapter 1. Analyzing spatiotemporal variation of land use and land cover data - p. 1 11. Chapter 2. Artificial Intelligence-based model fusion approach in hydroclimatic studies - p. 15 12. Chapter 3. Computations of probable maximum precipitation estimates - p. 43 13. Chapter 4. Deep learning: Long short-term memory in hydrological time series - p. 49 14. Chapter 5. Dimensionality reduction of correlated meteorological variables by Bayesian network-based graphical modeling - p. 69 15. Chapter 6. The ecohydrological function of the tropical forest rainfall interception: Observation and modeling - p. 77 16. Chapter 7. Emotional artificial neural network: A new ANN model in hydroinformatics - p. 105 17. Chapter 8. Exploring nature-based adaptation solutions for urban ecohydrology: Definitions, concepts, institutional framework, and demonstration - p. 117 18. Chapter 9. Fuzzy-based large-scale teleconnection modeling of monthly precipitation - p. 147 19. Chapter 10. Hydrologic models classification, calibration, and validation - p. 155 20. Chapter 11. Identification of soil erosion sites in semiarid zones: Using GIS, remote sensing, and PAP/RAC model - p. 169 21. Chapter 12. Metrics of the water performance engineering modeling - p. 191 22. Chapter 13. Outlier robust extreme learning machine: Predicting river water temperature in the absence of air temperature - p. 205 23. Chapter 14. Parametric and nonparametric methods for analyzing the trend of extreme events - p. 223 24. Chapter 15. Voting-based extreme learning machine: Potential of linking soil moisture content to soil temperature - p. 239 25. Chapter 16. Prediction of reference crop evapotranspiration: Empirical and machine learning approaches - p. 253 26. Chapter 17. Reference evapotranspiration in water requirement: Theory, concepts, and methods of estimation - p. 269 27. Chapter 18. Extremely randomized trees versus random forest, group method of data handling, and artificial neural network - p. 291 28. Chapter 19. Index of resilience and effectiveness of disaster risk management - p. 305 29. Chapter 20. Wavelet decomposition based on Gaussian process regression and multiple linear regression: Monthly reservoir evaporation prediction - p. 315 30. Chapter 21. Sequential Monte-Carlo methods in hydroclimatology - p. 327 31. Chapter 22. Smart cities and hydroinformatics - p. 341 32. Chapter 23. Support vector regression model optimized with GWO versus GA algorithms: Estimating daily pan-evaporation - p. 357 33. Chapter 24. Univariate, multivariate L-moments and copula functions for drought analysis - p. 375 34. Index - p. 389
Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode.
This is an interdisciplinary book, 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, 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. To Late George Edward Pelham Box (British Statistician: 1919–2013) - p. iii 7. Contributors - p. xiii 8. About the Editors - p. xvii 9. Preface - p. xviii 10. Chapter 1. Analyzing spatiotemporal variation of land use and land cover data - p. 1 11. Chapter 2. Artificial Intelligence-based model fusion approach in hydroclimatic studies - p. 15 12. Chapter 3. Computations of probable maximum precipitation estimates - p. 43 13. Chapter 4. Deep learning: Long short-term memory in hydrological time series - p. 49 14. Chapter 5. Dimensionality reduction of correlated meteorological variables by Bayesian network-based graphical modeling - p. 69 15. Chapter 6. The ecohydrological function of the tropical forest rainfall interception: Observation and modeling - p. 77 16. Chapter 7. Emotional artificial neural network: A new ANN model in hydroinformatics - p. 105 17. Chapter 8. Exploring nature-based adaptation solutions for urban ecohydrology: Definitions, concepts, institutional framework, and demonstration - p. 117 18. Chapter 9. Fuzzy-based large-scale teleconnection modeling of monthly precipitation - p. 147 19. Chapter 10. Hydrologic models classification, calibration, and validation - p. 155 20. Chapter 11. Identification of soil erosion sites in semiarid zones: Using GIS, remote sensing, and PAP/RAC model - p. 169 21. Chapter 12. Metrics of the water performance engineering modeling - p. 191 22. Chapter 13. Outlier robust extreme learning machine: Predicting river water temperature in the absence of air temperature - p. 205 23. Chapter 14. Parametric and nonparametric methods for analyzing the trend of extreme events - p. 223 24. Chapter 15. Voting-based extreme learning machine: Potential of linking soil moisture content to soil temperature - p. 239 25. Chapter 16. Prediction of reference crop evapotranspiration: Empirical and machine learning approaches - p. 253 26. Chapter 17. Reference evapotranspiration in water requirement: Theory, concepts, and methods of estimation - p. 269 27. Chapter 18. Extremely randomized trees versus random forest, group method of data handling, and artificial neural network - p. 291 28. Chapter 19. Index of resilience and effectiveness of disaster risk management - p. 305 29. Chapter 20. Wavelet decomposition based on Gaussian process regression and multiple linear regression: Monthly reservoir evaporation prediction - p. 315 30. Chapter 21. Sequential Monte-Carlo methods in hydroclimatology - p. 327 31. Chapter 22. Smart cities and hydroinformatics - p. 341 32. Chapter 23. Support vector regression model optimized with GWO versus GA algorithms: Estimating daily pan-evaporation - p. 357 33. Chapter 24. Univariate, multivariate L-moments and copula functions for drought analysis - p. 375 34. Index - p. 389
