Practical Hydroinformatics

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 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.

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.

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.

This book is designed as an undergraduate text for water and environmental engineering courses and as preliminary reading for postgraduate courses in water and environmental engineering- including introductory coverage of irrigation and drainage, water resources, hydrology, hydraulic structures, and more. The text and exercises have been classroom tested by undergraduate water and environmental engineering students and are augmented by material prepared for extramural short courses. It covers basic concepts of agricultural irrigation and drainage, including planning and design, surface intakes, economics, environmental impacts wetlands, and legal issues. Features: Numerous illustrations throughout to clarify the concepts presented Examines and compares the advantages and disadvantages of several methods of irrigation practice Explains the integral components including pumps, filters, piping, valves, and more Considers fertilizer application and nutrient management This comprehensive and well-illustrated book will be of great interest to students, professionals, and researchers involved with all aspects of water engineering, hydrology, and irrigation.

The Routledge Handbook of Geospatial Technologies and Society provides a relevant and comprehensive reference point for research and practice in this dynamic field. It offers detailed explanations of geospatial technologies and provides critical reviews and appraisals of their application in society within international and multi-disciplinary contexts as agents of change. The ability of geospatial data to transform knowledge in contemporary and future societies forms an important theme running throughout the entire volume. Contributors reflect on the changing role of geospatial technologies in society and highlight new applications that represent transformative directions in society and point towards new horizons. Furthermore, they encourage dialogue across disciplines to bring new theoretical perspectives on geospatial technologies, from neurology to heritage studies. The international contributions from leading scholars and influential practitioners that constitute the Handbook provide a wealth of critical examples of these technologies as agents of change in societies around the globe. The book will appeal to advanced undergraduates and practitioners interested or engaged in their application worldwide.