Advances in Streamflow Forecasting: From Traditional to Modern
Approaches covers the three major data-driven approaches of
streamflow forecasting including traditional approach of
statistical and stochastic time-series modelling with their recent
developments, stand-alone data-driven approach such as artificial
intelligence techniques, and modern hybridized approach where
data-driven models are combined with preprocessing methods to
improve the forecast accuracy of streamflows and to reduce the
forecast uncertainties. This book starts by providing the
background information, overview, and advances made in streamflow
forecasting. The overview portrays the progress made in the field
of streamflow forecasting over the decades. Thereafter, chapters
describe theoretical methodology of the different data-driven tools
and techniques used for streamflow forecasting along with case
studies from different parts of the world. Each chapter provides a
flowchart explaining step-by-step methodology followed in applying
the data-driven approach in streamflow forecasting. This book
addresses challenges in forecasting streamflows by abridging the
gaps between theory and practice through amalgamation of
theoretical descriptions of the data-driven techniques and
systematic demonstration of procedures used in applying the
techniques. Language of this book is kept simple to make the
readers understand easily about different techniques and make them
capable enough to straightforward replicate the approach in other
areas of their interest. This book will be vital for hydrologists
when optimizing the water resources system, and to mitigate the
impact of destructive natural disasters such as floods and droughts
by implementing long-term planning (structural and nonstructural
measures), and short-term emergency warning. Moreover, this book
will guide the readers in choosing an appropriate technique for
streamflow forecasting depending upon the given set of conditions.
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