This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
This authoritative book presents a comprehensive account of the important roles that nonlinear dynamic and chaos theories play in understanding, modeling and forecasting of hydrologic systems. This is achieved through the systematic presentation of: (1) the basic information on the salient characteristics of hydrologic systems and on the existing theories; (2) the fundamentals of nonlinear dynamic and chaos theories, and their identification and prediction; (3) a review of applications of these theories in hydrology; and (4) the scope and potential directions for the future. This book offers a bridge between the deterministic and the stochastic views in hydrology and is suitable as a textbook for hydrology courses.
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