Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques

The Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques provides a comprehensive overview of advanced machine learning methods and frameworks, covering theoretical foundations, computational and statistical properties, and performance guarantees. It covers topics such as nonparametric density estimation, nonparametric regression, Bayesian estimation, privacy, causality, stochastic learning algorithms, Cloud and Cluster Computing, Data Fusion Techniques, empirical orthogonal functions, teleconnection, Internet of Things, kernel-based modeling, large Eddy simulation, pattern recognition, uncertainty-based resiliency evaluation, and volume-based inverse mode. The book is interdisciplinary and is aimed at postgraduates and early-career researchers in computer science, mathematical science, applied science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering.

Format: Paperback / softback
Length: 450 pages
Publication date: 02 December 2022
Publisher: Elsevier Science Publishing Co Inc

The realm of advanced machine learning encompasses a wide array of theoretical foundations and cutting-edge methodologies employed in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques delves into both the artistry of designing effective learning algorithms and the science of scrutinizing an algorithm's computational and statistical characteristics, as well as its performance guarantees. A diverse group of global contributors, each an expert in their respective field, contribute to this comprehensive volume. They cover a range of theoretical foundational topics, including computational and statistical convergence rates, minimax estimation, concentration of measure, and more. Additionally, the book explores advanced machine learning methods such as nonparametric density estimation, nonparametric regression, and Bayesian estimation, as well as cutting-edge frameworks such as privacy, causality, and stochastic learning algorithms. Furthermore, the volume encompasses Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Pattern Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode. This interdisciplinary book appeals to a broad audience, including postgraduates and early-career researchers with interests spanning computer science, mathematical science, applied science, earth and geoscience, geography, civil engineering, engineering, water science, atmospheric science, social science, environment science, natural resources, and chemical engineering.

Dimension: 235 x 191 (mm)
ISBN-13: 9780128219614