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010   $a3-319-09235-9
024 7 $a10.1007/978-3-319-09235-5
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035   $a(EBL)1966875
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035   $a(PQKBTitleCode)TC0001386156
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035   $a(DE-He213)978-3-319-09235-5
035   $a(MiAaPQ)EBC1966875
035   $a(PPN)183093100
035   $a(EXLCZ)993710000000271819
100   $a20141103d2015      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aHydrological Data Driven Modelling$b[electronic resource] $eA Case Study Approach /$fby Renji Remesan, Jimson Mathew
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (261 p.)
225 1 $aEarth Systems Data and Models,$x2364-5830 ;$v1
300   $aDescription based upon print version of record.
311   $a3-319-09234-0 
320   $aIncludes bibliographical references at the end of each chapters and index.
327   $aIntroduction -- 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.
330   $aThis book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
410  0$aEarth Systems Data and Models,$x2364-5830 ;$v1
606   $aHydrogeology
606   $aHydrology
606   $aEngineering geology
606   $aEngineering?Geology
606   $aFoundations
606   $aHydraulics
606   $aHydrogeology$3https://scigraph.springernature.com/ontologies/product-market-codes/G19005
606   $aHydrology/Water Resources$3https://scigraph.springernature.com/ontologies/product-market-codes/211000
606   $aGeoengineering, Foundations, Hydraulics$3https://scigraph.springernature.com/ontologies/product-market-codes/T23020
615  0$aHydrogeology.
615  0$aHydrology.
615  0$aEngineering geology.
615  0$aEngineering?Geology.
615  0$aFoundations.
615  0$aHydraulics.
615 14$aHydrogeology.
615 24$aHydrology/Water Resources.
615 24$aGeoengineering, Foundations, Hydraulics.
676   $a55
676   $a551.4
676   $a551.48
676   $a624.15
700   $aRemesan$b Renji$4aut$4http://id.loc.gov/vocabulary/relators/aut$01061415
702   $aMathew$b Jimson$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299455103321
996   $aHydrological Data Driven Modelling$92518768
997   $aUNINA