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.
Through 25 peer-reviewed essays, scholars from the United States and Mexico delve into the environmental, social, economic, and cultural-historical components of what we call an environmental and tourism paradise - the region of Los Cabos, Baja California Sur. This region is vulnerable precisely because of the strong development pressure generated mainly by the tourism sector. Los Cabos analyzes these problems as an opportunity to contribute to the sustainable development of the region. Also available in Spanish, see Los Cabos: Prospectiva de un Paraíso Natural y Turístico. Published by San Diego State University Press and Institute for Regional Studies of the Californias
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.
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.