Changing climatic conditions, global warming trends, global population increase, water-related conflicts, and water shortages have resulted in changes in the water cycle, and hydrologic processes which were once thought to be simple are now known to be highly nonlinear. This compels the development of more sophisticated tools for enhanced and intensive water resources management. Data-driven tools have gained in popularity in recent years and have spawned a plethora of applications in water resources. Despite enjoying tremendous success in small-scale studies, there are very few applications to field-scale problems so far because various issues must be understood in order to make data-driven tools more practical to hydrologic applications. In the current research, three problem areas in hydrologic modeling have been identified that limit the applicability of data-driven tools: parameter specification, missing or incomplete data, and data compatibility. Each of these is studied in the present research, and solutions are provided. A new multiobjective calibration procedure in the form of Multiobjective Particle Swarm Optimization (MOPSO) is developed and tested. A solution to the problem of missing data is found through local least square imputation methodology. Furthermore, a downscaling algorithm is developed for the scale reconciliation problem. These tools are examined in various applications such as soil moisture forecasting, streamflow estimation, groundwater level forecasting, and downscaling of remotely sensed soil moisture. The current research only focuses on data-driven tools, and hence all the problems are examined in this same context. At the same time, the tools that are developed might well be appropriate for other modeling applications. This research addresses significant problems in the use of data-driven modeling tools so that they can be more effectively used in water resources management and hydrologic science. The results from the research show that the techniques developed and demonstrated here are sound and can help to remove some of the limitations in the use of data-driven tools, making them more attractive for application in hydrologic sciences.

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

This book is open access under a CC BY-NC 4.0 license. This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment. Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals. It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.

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.

The world is facing severe and growing challenges in maintainig water quality and meeting the rapidly growing demand for water resources. In addition, water used for irrigation, the largest use of water in most developing countries, will likely have to be diverted increasingly to meet the needs of urban areas and industry whilst remaining a prime engine of agricultural growth. Finally, environmental and other in-stream water demands become more important as economies develop. The river basin has been acknowledged to be the appropriate unit of analysis to address these chanllenges facing water resources management: and modeling at this scale can provide essential information for policy makers in their decisions on allication of resources. This paper reviews the state of the art of modeling approaches to integrated water resources management at the river basin scale, with particular focus on the potential of coupled economic hydrologic models, and concludes with directions for future modeling exercises.

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering. This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering. - Includes firsthand experience about artificial intelligence models, utilizing case studies - Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow - Presents the application of new instruments in water engineering