LEADER 02710oam 2200445zu 450 001 9910130837603321 005 20241212220438.0 010 $a9781467327954 010 $a1467327956 024 7 $a10.1109/ICCICC20556.2012 035 $a(CKB)3420000000000905 035 $a(SSID)ssj0000817879 035 $a(PQKBManifestationID)12369259 035 $a(PQKBTitleCode)TC0000817879 035 $a(PQKBWorkID)10831015 035 $a(PQKB)10501361 035 $a(NjHacI)993420000000000905 035 $a(EXLCZ)993420000000000905 100 $a20160829d2012 uy 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$a2012 11th IEEE International Conference on Cognitive Informatics and Cognitive Computing 210 31$a[Place of publication not identified]$cIEEE$d2012 215 $a1 online resource (ix, 541 pages) $cillustrations 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9781467327947 311 08$a1467327948 330 $aHow "wet" or "dry" a year is predicted to be has many impacts. Public utilities need to determine what percentage of their electric energy generation will be hydro power. Good water years enable the utilities to use more hydro power and, consequently, save oil. Conversely, in a dry year, the utilities must depend more on steam generation and therefore use more oil, coal, and atomic fuel. Agricultural interests use the information to determine crop planting patterns, ground water pumping needs, and irrigation schedules. Operators of flood control projects determine how much water can safely be stored in a reservoir while reserving space for predicted inflows. Municipalities use the information to evaluate their water supply and determine whether (in a dry year) water rationing may be needed. Currently a combination of linear regression equations and human judgment is used for producing these forecasts. In this paper, we describe a Support Vector Machine based method for river runoff forecasting. Our method uses Smola/Scholkopf's Sequential Minimal Optimization algorithm for training a Support Vector Machine with a RBF kernel. The experimental results on predicting the full natural flow of the American River at the Folsom Dam measurement station in California indicates that our method outperforms the current forecasting practices. 606 $aCognitive science$vCongresses 615 0$aCognitive science 676 $a153 702 $aieee 801 0$bPQKB 906 $aPROCEEDING 912 $a9910130837603321 996 $a2012 11th IEEE International Conference on Cognitive Informatics and Cognitive Computing$92506193 997 $aUNINA LEADER 02662nam 2200409Ka 450 001 9911046497003321 005 20250925100012.6 010 $a979-88-87199-50-4 035 $a(CKB)40368531100041 035 $a(ODN)ODN0012346963 035 $a(EXLCZ)9940368531100041 100 $a20250925d2025 uy 0 101 0 $arus 135 $aurcn|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a?????????? ?????????????? ???? xix ? ?????? ?? ???? $e«????? ??????» ????? ??????????????, ??????????????, ???? ? ????????- ??????? ?????????? ?? ????????? facebook (2017?2019). /$fSvetlana Gorshenina 210 $aLaVergne $cBiblioRossica$d2025 215 $a1 online resource 225 0 $aContemporary Western Rusistika. 300 $aTitle from eBook information screen.. 330 $a? ????? ???????????? ???????? ????????? ???????????, ??? ???????????? ?????????????????? Facebook ????? ????????? ?????????? ?????????? XIX ? ?????? XX ????. ??????????? ?????????? ??????? ??????????? ????????????????? ??????-????????? ? ?????????, ??????????? ??????? ???????????? ??????????? ????????????? ? ???????? ???????? ??????. ??? ?????????? ????????? ?????????? ?????? ????????? ??????-????????? ? ??????????????????, ?????????, ??????????????, ???????????????, (????)?????????? ? ? ???????????? ???????????? ?????????????? ?????? «???????? ???????» (Popular History). ?????? ???????? ????????? ??????????????... 606 $aNonfiction$2OverDrive 606 $aHistory$2OverDrive 606 $aPhotography$2OverDrive 606 $aSociology$2OverDrive 615 17$aNonfiction. 615 7$aHistory. 615 7$aPhotography. 615 7$aSociology. 686 $aHIS050000$aPHO000000$aSOC052000$2bisacsh 700 $aGorshenina$b Svetlana$0674278 906 $aBOOK 912 $a9911046497003321 996 $a?????????? ?????????????? ???? xix ? ?????? ?? ????$94466321 997 $aUNINA