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Hydrological data driven modelling =...

Remesan, Renji.

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Hydrological data driven modelling = a case study approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hydrological data driven modelling/ by Renji Remesan, Jimson Mathew.
Reminder of title:	a case study approach /
Author:	Remesan, Renji.
other author:	Mathew, Jimson.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xv, 250 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Hydrologic models. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-09235-5
ISBN:	9783319092355 (electronic bk.)

Hydrological data driven modelling = a case study approach /
Remesan, Renji.

Hydrological data driven modellinga case study approach /[electronic resource] :by Renji Remesan, Jimson Mathew. - Cham :Springer International Publishing :2015. - xv, 250 p. :ill. (some col.), digital ;24 cm. - Earth systems data and models ;v.1. - Earth systems data and models ;v.1..

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.

ISBN: 9783319092355 (electronic bk.)

Standard No.: 10.1007/978-3-319-09235-5doiSubjects--Topical Terms:

559363
Hydrologic models.

LC Class. No.: GB656.2.H9

Dewey Class. No.: 551.48011

Hydrological data driven modelling = a case study approach /
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100 1 $a Remesan, Renji. $3 2131734
245 1 0 $a Hydrological data driven modelling $h [electronic resource] : $b a case study approach / $c by Renji Remesan, Jimson Mathew.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xv, 250 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Earth systems data and models ; $v 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 Hydrologic models. $3 559363
650 1 4 $a Earth Sciences. $3 642591
650 2 4 $a Hydrogeology. $3 542813
650 2 4 $a Hydrology/Water Resources. $3 2055722
650 2 4 $a Geoengineering, Foundations, Hydraulics. $3 1001783
700 1 $a Mathew, Jimson. $3 1619757
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830 0 $a Earth systems data and models ; $v v.1. $3 2131735
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-09235-5
950 $a Earth and Environmental Science (Springer-11646)