Hydrological Data Driven Modelling : A Case Study Approach - nieuw boek

Boek bevindt zich in het datenbestand sinds 2014-12-30T18:24:10+01:00 (Amsterdam)
Detailpagina laatst gewijzigd op 2023-07-26T04:53:10+02:00 (Amsterdam)
ISBN/EAN: 9783319092355

ISBN - alternatieve schrijfwijzen:
3-319-09235-9, 978-3-319-09235-5
alternatieve schrijfwijzen en verwante zoekwoorden:
Auteur van het boek: jim, mathew
Titel van het boek: data, driven

Gegevens van de uitgever

Auteur: Renji Remesan; Jimson Mathew
Titel: Earth Systems Data and Models; Hydrological Data Driven Modelling - A Case Study Approach
Uitgeverij: Springer; Springer International Publishing
250 Bladzijden
Verschijningsjaar: 2014-11-03
Cham; CH
Taal: Engels
106,99 € (DE)
110,00 € (AT)
121,50 CHF (CH)
Available
XV, 250 p. 172 illus., 59 illus. in color.

EA; E107; eBook; Nonbooks, PBS / Geowissenschaften/Geologie; Geologie und die Lithosphäre; Verstehen; Applied hydrology; Artificial intelligence in hydrology; Evapotranspiration modelling; Hydrologic modelling; Rainfall-Runoff modelling; Solar radiation; Support vector; Time series modelling; hydrogeology; B; Geology; Water; Geoengineering; Earth and Environmental Science; Hydrologie und die Hydrosphäre; Konstruktiver Ingenieurbau, Baustatik; BB

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.

Introduction.- Hydroinformatics and Data based Modelling Issues in Hydrology.- Hydroinformatics and Data based Modelling Issues in Hydrology.- Model Data Selection and Data Pre-processing Approaches.- Machine Learning and Artificial Intelligence Based Approaches.- Data based Solar Radiation Modelling.- Data based Rainfall-Runoff Modelling.- Data based Evapotranspiration Modelling.- Application of Statistical Blockade in Hydrology.

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.