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.

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 book is a printed edition of the Special Issue "Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems" that was published in Water

This book focuses on threats, especially contaminants, to drinking water and the supply system, especially in municipalities but also in industrial and even residential settings. The safety, security, and suitability landscape can be described as dynamic and complex stemming from necessity and hence culpability due to the emerging threats and risks, vis-a-vis globalization resulting in new forms of contaminants being used due to new technologies. The book provides knowledge and guidance for engineers, scientists, designers, researchers, and students who are involved in water, sustainability, and study of security issues. This book starts out with basics of water usage, current statistics, and an overview ofwater resources. The book then introduces different scenarios of safety and security and areas that researchers need to focus. Following that, the book presents different types of contaminants – inadvertent, intentional, or incidental. The next section presents different methodologies of contamination sensing/detection and remediation strategies as per guidance and standards set globally. The book then concludes with selected chapters on water management, including critical infrastructure that is critical to maintaining safe water supplies to cities and municipalities. Each chapter includes descriptive information for professionals in their respective fields. The breadth of chapters offers insights into how science (physical, natural, and social) and technology can support new developments to manage the complexity resident within the evolving threat and risk landscape.

In a world experiencing increasingly intense hydrometeorological events driven by climate change, the need for effective solutions is paramount. Modeling and Monitoring Extreme Hydrometeorological Events presents a cutting-edge exploration of the challenges posed by flash droughts and floods, offering innovative methodologies and tools to address these global issues. Through a combination of computer modeling, remote sensing, artificial intelligence, and case studies, this book provides a comprehensive framework for understanding and mitigating the impacts of extreme hydrometeorological events. It examines the rapid emergence of flash droughts, which bring devastating consequences to agriculture, water resources, ecosystems, and public health. The book also delves into the complex dynamics of flash floods, exploring their causes, impacts, and potential solutions. With a focus on water management, the book addresses knowledge gaps, provides adaptation and mitigation strategies, and emphasizes the importance of climate change considerations. It aims to empower scientists, policymakers, professionals, and educators to develop effective policies and decision-making frameworks to combat the increasing risks posed by extreme hydrometeorological events. Written by a diverse team of experts in hydrology, hydrometeorology, emergency management, civil engineering, and related fields, this book offers valuable insights and practical tools for researchers, professors, graduate students, policymakers, and professionals.

This book gathers a collection of extended papers based on presentations given during the SimHydro 2017 conference, held in Sophia Antipolis, Nice, France on June 14–16, 2017. It focuses on how to choose the right model in applied hydraulics and considers various aspects, including the modeling and simulation of fast hydraulic transients, 3D modeling, uncertainties and multiphase flows. The book explores both limitations and performance of current models and presents the latest developments in new numerical schemes, high-performance computing, multiphysics and multiscale methods, and better interaction with field or scale model data. It gathers the lastest theoretical and innovative developments in the modeling field and presents some of the most advance applications on various water related topics like uncertainties, flood simulation and complex hydraulic applications. Given its breadth of coverage, it addresses the needs and interests of practitioners, stakeholders, researchers and engineers alike.

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.