Handbook of HydroInformatics: Volume I: Classic Soft-Computing Techniques explores the difference between traditional computing, also known as hard computing and soft computing, which is based on the importance given to issues like precision, certainty and rigor. Chapters define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo-Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, and more. This volume is a true interdisciplinary work that will be useful to post graduates and above in the fields of Artificial Intelligence, 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. Contains contributions from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, and more Presents applied examples and case studies in each chapter, providing the reader with real-world scenarios for comparison Defines classic soft-computing techniques that are necessary for a range of disciplines

"This book provides relevant theoretical frameworks and empirical research findings in the area hydroinformatics to assist professionals to improve their understanding of the development and use of decision support tools to support decision making and integrated water management at different organizational levels and domains"--Provided by publisher.

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.

Handbook of HydroInformatics Volume III: Water Data Management Best Practices presents the latest and most updated data processing techniques that are fundamental to Water Science and Engineering disciplines. These include a wide range of the new methods that are used in hydro-modeling such as Atmospheric Teleconnection Pattern, CONUS-Scale Hydrologic Modeling, Copula Function, Decision Support System, Downscaling Methods, Dynamic System Modeling, Economic Impacts and Models, Geostatistics and Geospatial Frameworks, Hydrologic Similarity Indices, Hydropower/Renewable Energy Models, Sediment Transport Dynamics Advanced Models, Social Data Mining, and Wavelet Transforms. This volume is an example of true interdisciplinary work. The audience includes postgraduates and above interested in Water Science, Geotechnical Engineering, Soil Science, Civil Engineering, Chemical Engineering, Computer Engineering, Engineering, Applied Science, Earth and Geoscience, Atmospheric Science, Geography, Environment Science, Natural Resources, Mathematical Science, and Social Sciences. It is a fully comprehensive handbook which provides all the information needed related to the best practices for managing water data. Contributions from global experts in the fields of data management research, climate change and resilience, insufficient data problem, etc. Thorough applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. Includes a wide range of new methods that are used in hydro-modeling, with step-by-step guides on how to use them.

"This book provides relevant theoretical frameworks and empirical research findings in the area hydroinformatics to assist professionals to improve their understanding of the development and use of decision support tools to support decision making and integrated water management at different organizational levels and domains"--Provided by publisher.

Ever-increasing population growth has caused a proportional increased demand for water, and existing water sources are depleting day by day. Moreover, with the impact of climate change, the rates of rainfall in many regions have experienced a higher degree of variability. In many cities, government utilities have been struggling to maintain sufficient water for the residents and other users. The Handbook of Irrigation Hydrology and Management: Irrigation Methods examines and analyzes irrigated ecosystems in which water storage, applications, or drainage volumes are artificially controlled in the landscape and the spatial domain of processes varies from micrometers to tens of kilometers, while the temporal domain spans from seconds to centuries. The continuum science of irrigation hydrology includes the surface, subsurface (unsaturated and groundwater systems), atmospheric, and plant subsystems. Further, the book addresses the best practices for various types of irrigation methods including pressure, smart, surface, and subsurface, and presents solutions for water scarcity and soil salinity in irrigation. Features: Offers water-saving strategies to increase the judicious use of scarce water resources Presents strategies to maximize agricultural yield per unit of water used for different regions Compares irrigation methods to offset changing weather patterns and impacts of climate change

A complete treatment of the theory and practice of groundwater engineering, The Handbook of Groundwater Engineering, Second Edition provides a current and detailed review of how to model the flow of water and the transport of contaminants both in the unsaturated and saturated zones, covers the production of groundwater and the remediation of contaminated groundwater.