Handbook of HydroInformatics

Volume II: Advanced Machine Learning Techniques

Saeid Eslamian, Faezeh Eslamian

• Publisher: Elsevier
• Year: 2022
• Print ISBN: 9780128219614
• eBook ISBN: 9780128219508
• Topic: Technology & Engineering
• Subtopic: Environmental Science
• Index: Technology & Engineering

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.

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• To Late George Edward Pelham Box (British Statistician: 1919–2013)
• Contributors
• About the Editors
• Preface
• Chapter 1 Analyzing spatiotemporal variation of land use and land cover data
• Chapter 2 Artificial Intelligence-based model fusion approach in hydroclimatic studies
• Chapter 3 Computations of probable maximum precipitation estimates
• Chapter 4 Deep learning: Long short-term memory in hydrological time series
• Chapter 5 Dimensionality reduction of correlated meteorological variables by Bayesian network-based graphical modeling
• Chapter 6 The ecohydrological function of the tropical forest rainfall interception: Observation and modeling
• Chapter 7 Emotional artificial neural network: A new ANN model in hydroinformatics
• Chapter 8 Exploring nature-based adaptation solutions for urban ecohydrology: Definitions, concepts, institutional framework, and demonstration
• Chapter 9 Fuzzy-based large-scale teleconnection modeling of monthly precipitation
• Chapter 10 Hydrologic models classification, calibration, and validation
• Chapter 11 Identification of soil erosion sites in semiarid zones: Using GIS, remote sensing, and PAP/RAC model
• Chapter 12 Metrics of the water performance engineering modeling
• Chapter 13 Outlier robust extreme learning machine: Predicting river water temperature in the absence of air temperature
• Chapter 14 Parametric and nonparametric methods for analyzing the trend of extreme events
• Chapter 15 Voting-based extreme learning machine: Potential of linking soil moisture content to soil temperature
• Chapter 16 Prediction of reference crop evapotranspiration: Empirical and machine learning approaches
• Chapter 17 Reference evapotranspiration in water requirement: Theory, concepts, and methods of estimation
• Chapter 18 Extremely randomized trees versus random forest, group method of data handling, and artificial neural network
• Chapter 19 Index of resilience and effectiveness of disaster risk management
• Chapter 20 Wavelet decomposition based on Gaussian process regression and multiple linear regression: Monthly reservoir evaporation prediction
• Chapter 21 Sequential Monte-Carlo methods in hydroclimatology
• Chapter 22 Smart cities and hydroinformatics
• Chapter 23 Support vector regression model optimized with GWO versus GA algorithms: Estimating daily pan-evaporation
• Chapter 24 Univariate, multivariate L-moments and copula functions for drought analysis
• Index