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.
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