Book Description

Attempts have been made to solve water resources engineering problems with the help of empirical, regression-based and numerical models. Empirical models are not universal, nor are the 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, practical and do not have the shortcomings of the existing methods. Artificial intelligence methods in this regard are the ones that meet this need. This book introduces the basics of artificial neural networks, fuzzy logic and genetic algorithms with illustrative examples. The applications of the artificial intelligence methods include, but not limited to, prediction of flood peaks, hydrographs, sedimentographs, seepage path, longitudinal dispersion coefficient in alluvial channels, mean and bankful discharge. The comparative analysis of the artificial intelligence methods against contemporary empirical, numerical, regression ones are also provided in the book.The target audiences for this book are graduate students, researchers, scientists and faculty members. However, the book can also be used as one of the core textbooks for undergraduate students.