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.
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