Название: Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques
Автор: Saeid Eslamian, Faezeh Eslamian
Издательство: Elsevier
Год: 2023
Страниц: 420
Язык: английский
Формат: pdf (true)
Размер: 43.7 MB

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.

Deep learning, defined as a subset of machine learning in artificial intelligence with artificial neural networks (ANN) that are able to learn without supervision and also known as deep neural network, has been popularly employed in the literature. Among Deep Learning techniques, long short-term memory (LSTM) models received much attention, especially in hydrological fields, due to their accurate predictive performance for long-term as well as short-term time interval. Developments of these models and their variants are described. Also described is the derivation to estimate the weights (or parameters) of LSTM models, along with training of their networks.

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.