ISBN: 9783319092355
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… Mehr…
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ISBN: 9783319092355
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… Mehr…
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ISBN: 9783319092355
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ISBN: 9783319092355
; PDF; Scientific, Technical and Medical > Earth sciences > Hydrology & the hydrosphere, Springer Berlin Heidelberg
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ISBN: 9783319092355
Hydrological Data Driven Modelling - A Case Study Approach: ab 106.99 € eBooks > Fachthemen & Wissenschaft > Wissenschaften allgemein Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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ISBN: 9783319092355
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… Mehr…
ISBN: 9783319092355
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… Mehr…
ISBN: 9783319092355
*Hydrological Data Driven Modelling* - A Case Study Approach / pdf eBook für 106.99 € / Aus dem Bereich: eBooks, Fachthemen & Wissenschaft, Wissenschaften, allgemein Medien > Bücher nein … Mehr…
ISBN: 9783319092355
; PDF; Scientific, Technical and Medical > Earth sciences > Hydrology & the hydrosphere, Springer Berlin Heidelberg
ISBN: 9783319092355
Hydrological Data Driven Modelling - A Case Study Approach: ab 106.99 € eBooks > Fachthemen & Wissenschaft > Wissenschaften allgemein Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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Detailangaben zum Buch - Hydrological Data Driven Modelling
EAN (ISBN-13): 9783319092355
ISBN (ISBN-10): 3319092359
Erscheinungsjahr: 2014
Herausgeber: Springer-Verlag GmbH
Buch in der Datenbank seit 2014-12-30T18:24:10+01:00 (Vienna)
Detailseite zuletzt geändert am 2023-07-26T04:53:10+02:00 (Vienna)
ISBN/EAN: 9783319092355
ISBN - alternative Schreibweisen:
3-319-09235-9, 978-3-319-09235-5
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: jim, mathew
Titel des Buches: data, driven
Daten vom Verlag:
Autor/in: Renji Remesan; Jimson Mathew
Titel: Earth Systems Data and Models; Hydrological Data Driven Modelling - A Case Study Approach
Verlag: Springer; Springer International Publishing
250 Seiten
Erscheinungsjahr: 2014-11-03
Cham; CH
Sprache: Englisch
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.
Covers many aspects of data based modelling issues with application to Hydrology Brings readers up to date with clear case studies Enables engineers to appropriately identify modelling approaches and issues Includes supplementary material: sn.pub/extras
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