2014, ISBN: 3319092340

[EAN: 9783319092348], Neubuch, [PU: Springer International Publishing Nov 2014], GEOLOGIE / HYDROGEOLOGIE; HYDROGEOLOGIE - HYDROLOGIE; BAU TIEFBAU; APPLIEDHYDROLOGY; ARTIFICIALINTELLIGENC… Plus…

[EAN: 9783319092348], Neubuch, [PU: Springer International Publishing Nov 2014], GEOLOGIE / HYDROGEOLOGIE; HYDROGEOLOGIE - HYDROLOGIE; BAU TIEFBAU; APPLIEDHYDROLOGY; ARTIFICIALINTELLIGENCEINHYDROLOGY; EVAPOTRANSPIRATIONMODELLING; HYDROLOGICMODELLING; RAINFALL-RUNOFFMODELLING; SOLARRADIATION; SUPPORTVECTOR; TIMESERIESMODELLING; HYDROGEOLOGY, This item is printed on demand - it takes 3-4 days longer - Neuware -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. 268 pp. Englisch, Books<

2014, ISBN: 9783319092348

[ED: Gebunden], [PU: Springer International Publishing], This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents … Plus…

[ED: Gebunden], [PU: Springer International Publishing], 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 268, [GW: 571g], 2015, Banküberweisung, PayPal, Klarna-Sofortüberweisung, [CT: Sonstiges / Sonstiges]<

2015, ISBN: 9783319092348

[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a ca… Plus…

[ED: Hardcover], [PU: Springer / Springer International Publishing / Springer, Berlin], 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. 2015. xv, 250 S. XV, 250 p. 172 illus., 59 illus. in color. 235 mm Versandfertig in 6-10 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Offene Rechnung (Vorkasse vorbehalten)<

2017, ISBN: 9783319092348

[ED: Buch], [PU: Springer International Publishing], Neuware - This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it pre… Plus…

[ED: Buch], [PU: Springer International Publishing], Neuware - 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.- Besorgungstitel - vorauss. Lieferzeit 3-5 Tage., DE, [SC: 2.70], Neuware, gewerbliches Angebot, 241x160x20 mm, 268, [GW: 571g], Banküberweisung, Offene Rechnung, Kreditkarte, PayPal, Offene Rechnung (Vorkasse vorbehalten), Internationaler Versand<

2015, ISBN: 3319092340

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… Plus…

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. Media Buch, 250 Seiten, Media > Books, Springer, 2015<