The hydrological sciences typically present grey or fuzzy information, making them quite messy and a choice challenge for fuzzy logic application. Providing readers with the first book to cover fuzzy logic modeling as it relates to water science, the author takes an approach that incorporates verbal expert views and other parameters that allow

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.

What is fuzzy logic?--a system of concepts and methods for exploring modes of reasoning that are approximate rather than exact. While the engineering community has appreciated the advances in understanding using fuzzy logic for quite some time, fuzzy logic's impact in non-engineering disciplines is only now being recognized. The authors of Fuzzy Logic in Geology attend to this growing interest in the subject and introduce the use of fuzzy set theory in a style geoscientists can understand. This is followed by individual chapters on topics relevant to earth scientists: sediment modeling, fracture detection, reservoir characterization, clustering in geophysical data analysis, ground water movement, and time series analysis.George Klir is the Distinguished Professor of Systems Science and Director of the Center for Intelligent Systems, Fellow of the IEEE and IFSA, editor of nine volumes, editorial board member of 18 journals, and author or co-author of 16 booksForeword by the inventor of fuzzy logic-- Professor Lotfi Zadeh

Arid and semi-arid regions are defined as areas where water is at its most scarce. The hydrological regime in these areas is extreme and highly variable, and they face great pressures to deliver and manage freshwater resources. However, there is no guidance on the decision support tools that are needed to underpin flood and water resource management in arid areas. UNESCO initiated the Global network for Water and Development Information for arid lands (GWADI), and arranged a workshop of the world's leading experts to discuss these issues. This book presents chapters from contributors to the workshop, and includes case studies from the world's major arid regions to demonstrate model applications, and web links to tutorials and state-of-the-art modelling software. This volume is a valuable reference for researchers and engineers working on the water resources of arid and semi-arid regions.

Engineers have attempted to solve water resources engineering problems with the help of empirical, regression-based and numerical models. Empirical models are not universal, nor are regression-based models. The numerical models are, on the other hand, physics-based but require substantial data measurement and parameter estimation. Hence, there is a need to employ models that are robust, user-friendly, and practical and that do not have the shortcomings of the existing methods. Artificial intelligence methods meet this need. Soft Computing in Water Resources Engineering introduces the basics of artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA). It gives details on the feed forward back propagation algorithm and also introduces neuro-fuzzy modelling to readers. Artificial intelligence method applications covered in the book include predicting and forecasting floods, predicting suspended sediment, predicting event-based flow hydrographs and sedimentographs, locating seepage path in an earth-fill dam body, and the predicting dispersion coefficient in natural channels. The author also provides an analysis comparing the artificial intelligence models and contemporary non-artificial intelligence methods (empirical, numerical, regression, etc.). The ANN, FL, and GA are fairly new methods in water resources engineering. The first publications appeared in the early 1990s and quite a few studies followed in the early 2000s. Although these methods are currently widely known in journal publications, they are still very new for many scientific readers and they are totally new for students, especially undergraduates. Numerical methods were first taught at the graduate level but are now taught at the undergraduate level. There are already a few graduate courses developed on AI methods in engineering and included in the graduate curriculum of some universities. It is expected that these courses, too, will soon be taught at the undergraduate levels.

Mathematical modelling has become an indispensable tool for engineers, scientists, planners, decision makers and many other professionals to make predictions of future scenarios as well as real impending events. As the modelling approach and the model to be used are problem specific, no single model or approach can be used to solve all problems, and there are constraints in each situation. Modellers therefore need to have a choice when confronted with constraints such as lack of sufficient data, resources, expertise and time. Environmental and Hydrological Systems Modelling provides the tools needed by presenting different approaches to modelling the water environment over a range of spatial and temporal scales. Their applications are shown with a series of case studies, taken mainly from the Asia-Pacific Region. Coverage includes: Population dynamics Reaction kinetics Water quality systems Longitudinal dispersion Time series analysis and forecasting Artificial neural networks Fractals and chaos Dynamical systems Support vector machines Fuzzy logic systems Genetic algorithms and genetic programming This book will be of great value to advanced students, professionals, academics and researchers working in the water environment.

Mathematical modelling has become an indispensable tool for engineers, scientists, planners, decision makers and many other professionals to make predictions of future scenarios as well as real impending events. As the modelling approach and the model to be used are problem specific, no single model or approach can be used to solve all problems, and there are constraints in each situation. Modellers therefore need to have a choice when confronted with constraints such as lack of sufficient data, resources, expertise and time. Environmental and Hydrological Systems Modelling provides the tools needed by presenting different approaches to modelling the water environment over a range of spatial and temporal scales. Their applications are shown with a series of case studies, taken mainly from the Asia-Pacific Region. Coverage includes: Population dynamics Reaction kinetics Water quality systems Longitudinal dispersion Time series analysis and forecasting Artificial neural networks Fractals and chaos Dynamical systems Support vector machines Fuzzy logic systems Genetic algorithms and genetic programming This book will be of great value to advanced students, professionals, academics and researchers working in the water environment.