LEADER 00949nam1-2200337---450- 001 990000754860203316 035 $a0075486 035 $aUSA010075486 035 $a(ALEPH)000075486USA01 035 $a0075486 100 $a20011120d1976----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aStato e confessioni religiose$fFrancesco Margiotta Broglio 210 $aFirenze$cLa nuova Italia$d1976- 215 $av$d20 cm 410 $12001 606 0 $aChiesa e stato$yItalia$xStoria 676 $a322.109 700 1$aMARGIOTTA BROGLIO,$bFrancesco$0160658 801 0$aIT$bsalbc$gISBD 912 $a990000754860203316 951 $aV A COLL. 85/$bLM$cV A COLL 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20011120$lUSA01$h1502 979 $c20020403$lUSA01$h1723 979 $aPATRY$b90$c20040406$lUSA01$h1652 996 $aStato e confessioni religiose$998622 997 $aUNISA LEADER 01664nam 2200469 450 001 9910574078503321 005 20231110225440.0 010 $a3-030-95066-2 035 $a(MiAaPQ)EBC7001365 035 $a(Au-PeEL)EBL7001365 035 $a(CKB)22898392000041 035 $a(EXLCZ)9922898392000041 100 $a20221216d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aImproving supply chains in the oil and gas industry $e12 modules to improve chronic challenges for maintenance, repair and operations /$fSanchay Roy and Stewart Dunbar 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dİ2022 215 $a1 online resource (111 pages) 225 1 $aSpringer Series in Supply Chain Management ;$vv.16 311 08$aPrint version: Roy, Sanchay Improving Supply Chains in the Oil and Gas Industry Cham : Springer International Publishing AG,c2022 9783030950651 320 $aIncludes bibliographical references and index. 410 0$aSpringer Series in Supply Chain Management 606 $aPetroleum pipelines 606 $aPetroleum industry and trade 606 $aBusiness logistics 615 0$aPetroleum pipelines. 615 0$aPetroleum industry and trade. 615 0$aBusiness logistics. 676 $a658.5 700 $aRoy$b Sanchay$01236965 702 $aDunbar$b Stewart 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910574078503321 996 $aImproving Supply Chains in the Oil and Gas Industry$92871709 997 $aUNINA LEADER 03922nam 22007695 450 001 9910299455103321 005 20200630053418.0 010 $a9783319092355 010 $a3319092359 024 7 $a10.1007/978-3-319-09235-5 035 $a(CKB)3710000000271819 035 $a(EBL)1966875 035 $a(SSID)ssj0001386156 035 $a(PQKBManifestationID)11830258 035 $a(PQKBTitleCode)TC0001386156 035 $a(PQKBWorkID)11351495 035 $a(PQKB)10892979 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 $eA Case Study Approach /$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 $aDescription based upon print version of record. 311 08$a9783319092348 311 08$a3319092340 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