Hydrological Data Driven Modelling A Case Study Approach /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Remesan, Renji (Autor), Mathew, Jimson (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Earth Systems Data and Models, 1
Temas:
Geology.
Water.
Hydrology.
Engineering geology.
Geoengineering.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Hydrological Data Driven Modelling  |h [electronic resource] :  |b A Case Study Approach /  |c by Renji Remesan, Jimson Mathew. 
250 |a 1st ed. 2015. 
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300 |a XV, 250 p. 172 illus., 59 illus. in color.  |b online resource. 
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490 1 |a Earth Systems Data and Models,  |x 2364-5849 ;  |v 1 
505 0 |a 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. 
520 |a 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. 
650 0 |a Geology. 
650 0 |a Water. 
650 0 |a Hydrology. 
650 0 |a Engineering geology. 
650 1 4 |a Geology. 
650 2 4 |a Water. 
650 2 4 |a Geoengineering. 
700 1 |a Mathew, Jimson.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319092362 
776 0 8 |i Printed edition:  |z 9783319092348 
776 0 8 |i Printed edition:  |z 9783319350288 
830 0 |a Earth Systems Data and Models,  |x 2364-5849 ;  |v 1 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-09235-5  |z Texto Completo 
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912 |a ZDB-2-SXEE 
950 |a Earth and Environmental Science (SpringerNature-11646) 
950 |a Earth and Environmental Science (R0) (SpringerNature-43711) 

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