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Hydrological data driven modelling[electronic resource] :a case study approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	551.48011
書名/作者:	Hydrological data driven modelling : a case study approach // by Renji Remesan, Jimson Mathew.
作者:	Remesan, Renji.
其他作者:	Mathew, Jimson.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xv, 250 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Hydrologic models.
標題:	Earth Sciences.
標題:	Hydrogeology.
標題:	Hydrology/Water Resources.
標題:	Geoengineering, Foundations, Hydraulics.
ISBN:	9783319092355 (electronic bk.)
ISBN:	9783319092348 (paper)
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-09235-5

Hydrological data driven modelling[electronic resource] :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:

567829
Hydrologic models.

LC Class. No.: GB656.2.H9

Dewey Class. No.: 551.48011

Hydrological data driven modelling[electronic resource] :a case study approach /
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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.
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