Handbook of HydroInformatics

Volume II: Advanced Machine Learning Techniques

Häftad, Engelska, 2022

AvSaeid Eslamian,Faezeh Eslamian,Iran) Eslamian, Saeid (Distinguished Full Professor, Disaster Relief: Resilient and Sustainable Water Resources, Isfahan University of Technology,Canada) Eslamian, Faezeh, Ph.D., CEP, (Project Manager, GHD, Quebec

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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.

Key insights from 24 contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc.�
Offers 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.

Produktinformation

Utgivningsdatum2022-12-09
Mått216 x 276 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor418
FörlagElsevier Science
ISBN9780128219614

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Miljöteknik inom Naturvetenskap och teknik

Saeid Eslamian received his PhD in Civil and Environmental Engineering from University of New South Wales, Australia in 1998. Saeid was Visiting Professor in Princeton University and ETH Zurich in 2005 and 2008 respectively. He has contributed to more than 1K publications in journals, conferences, books. Eslamian has been appointed as 2-Percent Top Researcher by Stanford University for several years. Currently, he is full professor of Hydrology and Water Resources and Director of Excellence Center in Risk Management and Natural Hazards. Isfahan University of Technology, His scientific interests are Floods, Droughts, Water Reuse, Climate Change Adaptation, Sustainability and ResilienceFaezeh Eslamian is a PhD holder of bioresource engineering from McGill University. Her research focuses on the development of a novel lime-based product to mitigate phosphorus loss from agricultural fields. Faezeh completed her bachelor’s and master’s degrees in civil and environmental engineering from Isfahan University of Technology, Iran, where she evaluated natural and low-cost absorb bents for the removal of pollutants such as textile dyes and heavy metals. Furthermore, she has conducted research on the worldwide water quality standards and wastewater reuse guidelines. Faezeh is an experienced multidisciplinary researcher with research interests in soil and water quality, environmental remediation, water reuse, and drought management.

35. Bayesian Estimation36. Cloud and Cluster Computing37. Computational and Statistical Convergence Rates38. Concentration of Measure39. Cross Validation40. Data Assimilation41. Data Fusion Techniques42. Deep Learning43. Empirical Orthogonal Functions44. Empirical Orthogonal Teleconnection45. Error Modeling46. GARCH Time Series Analysis47. Gradient-Based Optimization48. Internet-Based Methods49. Internet of Things50. Kernel-Based Modeling51. Large Eddy Simulation52. Markov Chain Monte Carlo Methods53. Minimax Estimation54. Model Fusion Approach55. Monitoring Quality Sensors56. Nested Reinforcement Learning57. Nested Stochastic Dynamic Programming58. Nonparametric Density estimation59. Nonparametric Regressions60. Operational Real-Time Forecasting61. Patter Recognition62. Self-Adaptive Evolutionary Extreme Learning Machine63. Stochastic Learning Algorithms64. Supercomputing Methods (Parallelization/GPU)65. Transient-Based Time-Frequency Analysis66. Uncertainty-Based Resiliency Evaluation67. Volume-Based Inverse Mode68. WebGIS

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