Vai al contenuto principale della pagina

Hydrological Data Driven Modelling : A Case Study Approach / / by Renji Remesan, Jimson Mathew



(Visualizza in formato marc)    (Visualizza in BIBFRAME)

Autore: Remesan Renji Visualizza persona
Titolo: Hydrological Data Driven Modelling : A Case Study Approach / / by Renji Remesan, Jimson Mathew Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Edizione: 1st ed. 2015.
Descrizione fisica: 1 online resource (261 p.)
Disciplina: 55
551.4
551.48
624.15
Soggetto topico: Hydrogeology
Hydrology
Engineering geology
Engineering—Geology
Foundations
Hydraulics
Hydrology/Water Resources
Geoengineering, Foundations, Hydraulics
Persona (resp. second.): MathewJimson
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Hydrological Data Driven Modelling  Visualizza cluster
ISBN: 3-319-09235-9
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910299455103321
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Serie: Earth Systems Data and Models, . 2364-5830 ; ; 1