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Handbook of HydroInformatics Volume II: Advanced Machine Learning Techniques

Eslamian Saeid
Heftet / 2022 / Engelsk

Produktdetaljer

ISBN
9780128219614
Publisert
2022-12-09
Utgiver
Vendor
Elsevier Science Publishing Co Inc
Vekt
450 gr
Høyde
276 mm
Bredde
216 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
418

Redaktør
Eslamian Saeid

Om bidragsyterne

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 Resilience Faezeh 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.

Handbook of HydroInformatics Volume II: Advanced Machine Learning Techniques

Eslamian Saeid
Heftet / 2022 / Engelsk
Eslamian Saeid
Heftet / 2022 / Engelsk
Nettpris:
2.326,-
Levering 7-20 dager
Sjekk lagerstatus i butikk
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.
Les mer
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
Les mer
A fully comprehensive handbook that provides all the information needed on Advanced Machine Learning Techniques
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.
Les mer

Relaterte produkter

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.
Les mer
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
Les mer
A fully comprehensive handbook that provides all the information needed on Advanced Machine Learning Techniques
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.
Les mer

Produktdetaljer

ISBN
9780128219614
Publisert
2022-12-09
Utgiver
Vendor
Elsevier Science Publishing Co Inc
Vekt
450 gr
Høyde
276 mm
Bredde
216 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
418

Redaktør
Eslamian Saeid

Om bidragsyterne

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 Resilience Faezeh 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.

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