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Hybrid Models for Hydrological Forecasting
This book presents the investigation of possibilities and different architectures of integrating hydrological knowledge and conceptual models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid models. The book addresses the following specific topics: A classification of different hybrid modelling approaches in the context of flow forecasting.The methodological development and application of modular models based on clustering and baseflow empirical formulations.The integration of hydrological conceptual models with neural network error corrector models and the use of committee models for daily streamflow forecasting.The application of modular modelling and fuzzy committee models to the problem of downscaling weather information for hydrological forecasting. The results of this research show the increased forecasting accuracy when modular models, which integrate conceptual and data-driven models, are considered. Committee machine modelling show to be able to manage increased lead time with an acceptable accuracy.
Advanced Hydroinformatics
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 in...
Advanced Hydroinformatics
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 in...
Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management
This book discusses the problems in planning, building, and management strategies in the wake of application and expansion of remote sensing and GIS products in natural resources and infrastructure management. The book suggests proactive solutions to problems of natural resources and infrastructure management, providing alternatives for strategic planning, effective delivery, and growth perspectives. The uniqueness of the book is its broader spectrum of coverage with related interconnections and interdependences across science, engineering, and innovation. The book contains information that can be downscaled to the local level. Presenting a wide spectrum of viewpoints and approaches, the boo...
Hydrology and Best Practices for Managing Water Resources in Arid and Semi-Arid Lands
The management of water resources is extremely important for survival. Depending on the climate, certain regions require different strategies to maintain sustainable hydrological systems. Hydrology and Best Practices for Managing Water Resources in Arid and Semi-Arid Lands is a crucial scholarly resource that outlines current trends in water management and offers solutions for the future of this growing field. Highlighting pertinent topics such as hydrological processes modelling, satellite hydrology, water pollution, and climate resources, this publication is ideal for environmental engineers, academicians, graduate students, and researchers that are eager to discover more about the issues and processes currently shaping water management technology.
Hydrology and Water Resources Management in Arid, Semi-Arid, and Tropical Regions
Hydrology is a key influence on water security, environmental sustainability, agricultural production, energy, and transport, especially in unique environments such as arid regions and the tropics, where degradation issues on water and land can threaten the livelihoods of poor communities. With implications in urbanization, landscape architecture, and sanitation, enhancing the practice of water use, management, and planning is imperative for the sustainable development of these regions. Hydrology and Water Resources Management in Arid, Semi-Arid, and Tropical Regions is an essential research publication that seeks to improve scientific understanding and sharing of data in hydrology and integrated water resources management of arid, semi-arid, and tropical regions in order to enhance water governance and alleviate reduction in the vulnerability of water resources systems to global changes. Featuring a wide range of topics such as hydrometeorology, sustainable development, and climate change, this book is ideal for researchers, technology developers, academicians, policymakers, government officials, and students.
Spatiotemporal Analysis of Extreme Hydrological Events
Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling, neural network and evolutionary algorithms. This important resource for both hydrologists and statisticians interested in the framework of spatial and temporal analysis of hydrological events will provide users with an enhanced understanding of the relationship between magnitude, dynamics and the probability of extreme hydrological events. - Presents spatio-temporal processes, including multivariate dynamic modelling - Provides varying methodological approaches, giving the readers multiple hydrological modelling information to use in their work - Includes a variety of case studies making the context of the book relatable to everyday working situations
Integrating Multiple Sources of Information for Improving Hydrological Modelling: an Ensemble Approach
The availability of Earth observation and numerical weather prediction data for hydrological modelling and water management has increased significantly, creating a situation that today, for the same variable, estimates may be available from two or more sources of information. Yet, in hydrological modelling, usually, a particular set of catchment characteristics and input data is selected, possibly ignoring other relevant data sources. In this thesis, therefore, a framework is being proposed to enable effective use of multiple data sources in hydrological modelling. In this framework, each available data source is used to derive catchment parameter values or input time series. Each unique com...
Hydrological Drought
The majority of the examples are taken from regions where the rivers run most of the year.
