Handbook of HydroInformatics
Volume II: Advanced Machine Learning Techniques
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Advanced Machine Learning Techniques: 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. Global contributors cover theoretical foundation topics such as computational and statistical convergence rates, minimax estimation and concentration of measure. Advanced machine learning methods such as nonparametric density estimation, nonparametric regression, and Bayesian estimation, as well as advanced frameworks such as privacy, causality and stochastic learning algorithms are also included. Other methods covered include 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, making this word an interdisciplinary guide that will appeal to post graduates interested in Computer Science, Artificial Intelligence, 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 Features
- Contains contributions from the fields of data management research, climate change and resilience, insufficient data problem, and more
- Presents applied examples and case studies in each chapter, providing the reader with real-world scenarios for comparison
- Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees
Readership
Post graduates and above 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
Product details
- No. of pages: 450
- Language: English
- Copyright: © Elsevier 2022
- Published: October 1, 2022
- Imprint: Elsevier
- Paperback ISBN: 9780128219614
About the Editors
Saeid Eslamian
Saeid Eslamian is a Full Professor of Hydrology and Water Resources Sustainability at Isfahan University of Technology in the Department of Water Engineering. His research focuses mainly on Statistical and Environmental Hydrology and Climate Change. In particular, he is working on Modeling Natural Hazards including Flood, Drought, Storm, Wind, and Pollution toward a sustainable environment. Formerly, he was a Visiting Professor at Princeton University, United States, University of ETH Zurich, Switzerland, and McGill University, Montreal, Quebec, Canada. He has contributed to more than 400 publications in journals, books, or as technical reports. He is the Founder and Chief Editor of two journals and has been the author of about 100 books and chapters. Prof. Eslamian is the editorial board member and reviewer of about 40 Web of Science (ISI) Journals.
Affiliations and Expertise
Full Professor of Hydrology and Water Resources Sustainability, Department of Water Engineering, Isfahan University of Technology, Iran
Faezeh Eslamian
Faezeh Eslamian works at McHill University in Quebec, Canada.
Affiliations and Expertise
McHill University, Quebec, Canada