Hydroinformatics is an emerging subject that is expected to gather speed, momentum and critical mass throughout the forthcoming decades of the 21st century. This book provides a broad account of numerous advances in that field - a rapidly developing discipline covering the application of information and communication technologies, modelling and computational intelligence in aquatic environments. A systematic survey, classified according to the methods used (neural networks, fuzzy logic and evolutionary optimization, in particular) is offered, together with illustrated practical applications for solving various water-related issues. ...

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.

"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 Hydroinformatics Advanced Hydroinformatics Machine Learning and Optimization for Water Resources The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts. Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management. Volume Highlights Include: Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics Advances in modeling hydrological systems Different data analysis methods and models for forecasting water resources New areas of knowledge discovery and optimization based on using machine learning techniques Case studies from North America, South America, the Caribbean, Europe, and Asia The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

This authoritative book presents a comprehensive account of the essential roles of nonlinear dynamic and chaos theories in understanding, modeling, and forecasting hydrologic systems. This is done through a systematic presentation of: (1) information on the salient characteristics of hydrologic systems and on the existing theories for their modeling; (2) the fundamentals of nonlinear dynamic and chaos theories, methods for chaos identification and prediction, and associated issues; (3) a review of the applications of chaos theory in hydrology; and (4) the scope and potential directions for the future. This book bridges the divide between the deterministic and the stochastic schools in hydrology, and is well suited as a textbook for hydrology courses.

This book summarizes the latest research and developments related to the application of nature-inspired metaheuristic algorithms coupled with artificial neural networks (ANNs) in hydrology. The book covers the theoretical foundations, models and methods, structure, frameworks and analysis of applying novel ANNs in hydrology. It starts with the introduction of ANNs as a black box model, followed by the coupling of various metaheuristic algorithms with ANNs to form novel neural network models for solving real-world problems in hydrology, including Particle Swarm Optimization (PSO) for rainfall-runoff modeling, Bat Optimization (Bat) and Cuckoo Search Optimization (CSO) for future rainfall prediction, the Whale Optimization Algorithm (WOA) and Salp Swarm Optimization (SSO) for future water level prediction, Grey Wolf Optimization (GWO), Multi-Verse Optimization (MVO), the Sine Cosine Algorithm (SCA) and the Hybrid Sine Cosine and Fitness Dependent Optimizer (SC-FDO) for imputing missing rainfall data.

This book provides a comprehensive approach to all aspects of water-related subjects affected by climate change that expand readers' attitudes toward future of the management strategies and improve management plans. It summarizes climate change scenarios, models, downscaling methods, and how to select the appropriate method. It also introduces practical steps in assessing climate change impacts on water issues through introducing hydrological models and climate change data applications in hydrologic analysis. The book caters to specialist readers who are interested in analyzing climate change effects on water resources, and related issues can gain a profound understanding of the practical concepts and step-by-step analysis, which is enriched with real case studies all around the world. Moreover, readers will be familiar with potential mitigation and adaptation measures in sustainable water engineering, considering the results of hydrologic modeling.

The effects of climate change, rapid urbanization, and aging infrastructure challenge water policymakers to confront a radical paradigm shift in water resources utilization. Recent advances in sensing, networking, processing, and control have provided the means for sustainable solutions in water management, and their implementation in water infrastructures is collectively referred to as "smart water grids." Smart water grids depend upon cyber-physical system principles to effectively respond to issues regarding the scalability and reliability of dynamic and inaccessible environments. As such, unique smart water grid issues associated with front-end signal processing, communication, control, and data analysis must be jointly addressed, while sophisticated techniques for data analytics must be introduced into cyber-physical systems research. This book provides a thorough description of the best practices for designing and implementing cyber-physical systems that are tailored to different aspects of smart water grids. It is organized into three distinct, yet complementary areas, namely: the theory behind water-oriented cyber-physical systems with an emphasis on front-end sensing and processing, communication technologies, and learning techniques over water data; the applications and emerging topics of cyber-physical systems for water urban infrastructures, including real-life deployments, modern control tools, and economic aspects for smart water grids; and the applications and emerging topics across natural environments, emphasizing the evolution of fresh water resources. The structured discussion yields a rich, comprehensive body of knowledge on this emerging topic of research and engineering. As water issues intensify on a global scale, this book offers an algorithmic and practical toolkit for intermediate and advanced readers as well as professionals and researchers who are active in, or interested in, learning more about smart water grids. Key Features: Emphasizes the multidisciplinary nature of this emerging topic, covering both theoretical and practical aspects of this area while providing insights on existing deployments, which can serve as design examples for new applications. Explores how modern signal processing and machine learning techniques can contribute and enrich the potential of smart water grids, well beyond conventional closed-loop control techniques. Highlights complementary aspects that will help shape the future of smart water grids, such as consumption awareness, economic aspects, and control tools in industrial water treatment as well as the impact of climate change on fresh water resources. Enables the reader to better understand this emerging topic, investing in current state-of-the-art and future technological roadmaps for smart water grids.

A revision of Openshaw and Abrahart's seminal work, GeoComputation, Second Edition retains influences of its originators while also providing updated, state-of-the-art information on changes in the computational environment. In keeping with the field's development, this new edition takes a broader view and provides comprehensive coverage across the

Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.