Handbook of HydroInformatics
Volume II: Advanced Machine Learning Techniques
- 1st Edition - December 6, 2022
- Imprint: Elsevier
- Editors: Saeid Eslamian, Faezeh Eslamian
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 9 6 1 - 4
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 9 5 0 - 8
Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Ha… Read more
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Request a sales quoteAdvanced 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.
Post graduates and above 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
- Cover
- 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
- Abstract
- Acknowledgment
- 1: Introduction
- 2: Data preparation
- 3: Visual interpretations
- 4: LULC distribution
- 5: LULC change detection
- 6: Image interpretation
- 7: LAI model
- 8: Compare the visual interpretation vs image interpretation
- 9: Conclusions
- References
- Chapter 2: Artificial Intelligence-based model fusion approach in hydroclimatic studies
- Abstract
- 1: Introduction
- 2: Mathematical concepts
- 3: Some applications
- 4: Conclusions
- References
- Chapter 3: Computations of probable maximum precipitation estimates
- Abstract
- 1: Introduction
- 2: Methodology of PMP estimation
- 3: Statistical PMP estimates: A case-study
- 4: Conclusions
- References
- Chapter 4: Deep learning: Long short-term memory in hydrological time series
- Abstract
- 1: Introduction
- 2: Model description of long short-term memory (LSTM)
- 3: Training network and backpropagation
- 4: Variants of LSTM
- 5: Normalization and hyperparameter selection
- 6: LSTM applications in hydrometeorological variables
- 7: Employed deep learning programs for LSTM
- 8: Conclusions
- References
- Chapter 5: Dimensionality reduction of correlated meteorological variables by Bayesian network-based graphical modeling
- Abstract
- 1: Introduction
- 2: Study area and data used
- 3: Methodology
- 4: Results and discussions
- 5: Conclusions
- References
- Chapter 6: The ecohydrological function of the tropical forest rainfall interception: Observation and modeling
- Abstract
- 1: Introduction
- 2: Canopy water balance: concepts and general aspects of the monitoring
- 3: Measurements of the rainfall interception components
- 4: Rainfall interception modeling
- 5: Conclusions
- References
- Chapter 7: Emotional artificial neural network: A new ANN model in hydroinformatics
- Abstract
- 1: Introduction
- 2: Mathematical concepts of emotional artificial neural network
- 3: Some applications of EANN
- 4: Conclusions
- References
- Chapter 8: Exploring nature-based adaptation solutions for urban ecohydrology: Definitions, concepts, institutional framework, and demonstration
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Nature-based adaptation solutions (NBaS): Conceptual framework and position
- 3: NbaS and ecohydrology
- 4: The need for physically-based evidence
- 5: Conclusions
- References
- Chapter 9: Fuzzy-based large-scale teleconnection modeling of monthly precipitation
- Abstract
- 1: Introduction
- 2: Materials and methods
- 3: Results and discussion
- 4: Conclusions
- References
- Chapter 10: Hydrologic models classification, calibration, and validation
- Abstract
- 1: Introduction
- 2: Hydrological modeling for integrated water management
- 3: Model calibration and validation
- 4: Conclusions
- References
- Further reading
- Chapter 11: Identification of soil erosion sites in semiarid zones: Using GIS, remote sensing, and PAP/RAC model
- Abstract
- 1: Introduction
- 2: Materials and method
- 3: Results and discussion
- 4: Conclusions
- References
- Chapter 12: Metrics of the water performance engineering modeling
- Abstract
- 1: Introduction
- 2: Types of hydro-climatological modeling and metrics
- 3: Some applications of the metrics
- 4: Agenda for future studies
- References
- Chapter 13: Outlier robust extreme learning machine: Predicting river water temperature in the absence of air temperature
- Abstract
- 1: Introduction
- 2: Study area and data
- 3: Materials and methods
- 4: Results and discussion
- 5: Conclusions
- References
- Chapter 14: Parametric and nonparametric methods for analyzing the trend of extreme events
- Abstract
- 1: Introduction
- 2: Trend calculation methods
- 3: Case studies
- 4: Conclusions
- References
- Chapter 15: Voting-based extreme learning machine: Potential of linking soil moisture content to soil temperature
- Abstract
- 1: Introduction
- 2: Materials and methods
- 3: Results and discussion
- 4: Conclusions
- References
- Chapter 16: Prediction of reference crop evapotranspiration: Empirical and machine learning approaches
- Abstract
- 1: Introduction
- 2: An overview of various empirical methods for reference evapotranspiration estimation
- 3: Estimation of ET0 using empirical models
- 4: Machine learning techniques used for estimation of evapotranspiration
- 5: Modeling of ET0 using machine learning techniques
- 6: Deep learning techniques in modeling of ET0
- 7: Case study
- 8: Conclusions
- References
- Chapter 17: Reference evapotranspiration in water requirement: Theory, concepts, and methods of estimation
- Abstract
- 1: Introduction
- 2: Theory of evapotranspiration
- 3: Methods of calculating the reference evapotranspiration
- 4: Conclusions
- References
- Chapter 18: Extremely randomized trees versus random forest, group method of data handling, and artificial neural network
- Abstract
- 1: Introduction
- 2: Study area and data
- 3: Methodology
- 4: Results and discussion
- 5: Conclusions
- References
- Chapter 19: Index of resilience and effectiveness of disaster risk management
- Abstract
- 1: Introduction
- 2: The disaster risk management index, DRMi
- 3: Evaluation for Latin America and the Caribbean region
- 4: Conclusions
- References
- Chapter 20: Wavelet decomposition based on Gaussian process regression and multiple linear regression: Monthly reservoir evaporation prediction
- Abstract
- 1: Introduction
- 2: Materials and method
- 3: Evaluation criteria
- 4: Results and discussion
- 5: Conclusions
- References
- Chapter 21: Sequential Monte-Carlo methods in hydroclimatology
- Abstract
- 1: Introduction
- 2: Bayes’ theorem
- 3: Basics of sequential Monte-Carlo methods
- 4: Particle filters for high-dimensional geoscience applications
- 5: Adaptive estimation using particle filter
- 6: Conclusions
- References
- Chapter 22: Smart cities and hydroinformatics
- Abstract
- 1: The history of urbanization and smart cities
- 2: Definition of a Smart City
- 3: Components of a Smart City
- 4: Smart cities and internet of things
- 5: Smart City strategies
- 6: Smart cities and hydroinformatics
- 7: Conclusions
- References
- Chapter 23: Support vector regression model optimized with GWO versus GA algorithms: Estimating daily pan-evaporation
- Abstract
- 1: Introduction
- 2: Study location and data collection
- 3: Methodology
- 4: Results and discussion
- 5: Conclusions
- References
- Chapter 24: Univariate, multivariate L-moments and copula functions for drought analysis
- Abstract
- 1: Introduction
- 2: Materials and methods
- 3: Results
- 4: Conclusions
- References
- Index
- Edition: 1
- Published: December 6, 2022
- No. of pages (Paperback): 418
- No. of pages (eBook): 418
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780128219614
- eBook ISBN: 9780128219508
SE
Saeid Eslamian
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
Affiliations and expertise
Distinguished Full Professor, Disaster Relief: Resilient and Sustainable Water Resources, Isfahan University of Technology, IranFE
Faezeh Eslamian
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.
Affiliations and expertise
Project Manager, GHD, Quebec, CanadaRead Handbook of HydroInformatics on ScienceDirect