Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques

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.

Table of Contents:
35. Bayesian Estimation 36. Cloud and Cluster Computing 37. Computational and Statistical Convergence Rates 38. Concentration of Measure 39. Cross Validation 40. Data Assimilation 41. Data Fusion Techniques 42. Deep Learning 43. Empirical Orthogonal Functions 44. Empirical Orthogonal Teleconnection 45. Error Modeling 46. GARCH Time Series Analysis 47. Gradient-Based Optimization 48. Internet-Based Methods 49. Internet of Things 50. Kernel-Based Modeling 51. Large Eddy Simulation 52. Markov Chain Monte Carlo Methods 53. Minimax Estimation 54. Model Fusion Approach 55. Monitoring Quality Sensors 56. Nested Reinforcement Learning 57. Nested Stochastic Dynamic Programming 58. Nonparametric Density estimation 59. Nonparametric Regressions 60. Operational Real-Time Forecasting 61. Patter Recognition 62. Self-Adaptive Evolutionary Extreme Learning Machine 63. Stochastic Learning Algorithms 64. Supercomputing Methods (Parallelization/GPU) 65. Transient-Based Time-Frequency Analysis 66. Uncertainty-Based Resiliency Evaluation 67. Volume-Based Inverse Mode 68. WebGIS