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2010, ISBN: 9789048154210

[ED: Softcover], [PU: Springer Netherlands], R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The b… Meer...

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ISBN: 9789048154210

R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (AN… Meer...

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Artificial Neural Networks in Hydrology - pocketboek

2010

ISBN: 9789048154210

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Erscheinungsdatum: 07.12.2010, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Artificial Neural Networks in Hydrology, Auflage: Softcover reprint of hardcover 1st ed. 2000,… Meer...

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2000, ISBN: 9048154219

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2000, ISBN: 9048154219

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Bijzonderheden over het boek
Artificial Neural Networks in Hydrology

The past decade has witnessed a flurry of hydrologic research activity related to artificial neural networks (ANNs). This volume is a compilation of chapters that have been contributed by researchers from several countries, and represents a wide spectrum of ANN applications in hydrology. Future potential of ANN applications has been identified at appropriate places. Readers of the book will find chapters dealing with preliminary aspects as well as advanced features of ANNs. With a unique focus towards hydrologic applications, this book will serve as a valuable reference for graduate students, research workers, and professionals interested in learning more about this computational tool. The goal of this book is to help ANNs find greater acceptability among researchers and practising hydrologists alike.

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EAN (ISBN-13): 9789048154210
ISBN (ISBN-10): 9048154219
Gebonden uitgave
pocket book
Verschijningsjaar: 2010
Uitgever: Springer-Verlag/Sci-Tech/Trade
352 Bladzijden
Gewicht: 0,532 kg
Taal: eng/Englisch

Boek bevindt zich in het datenbestand sinds 2012-02-15T15:29:30+01:00 (Amsterdam)
Detailpagina laatst gewijzigd op 2020-09-30T20:38:38+02:00 (Amsterdam)
ISBN/EAN: 9789048154210

ISBN - alternatieve schrijfwijzen:
90-481-5421-9, 978-90-481-5421-0


Gegevens van de uitgever

Auteur: R.S. Govindaraju; A.R. Rao
Titel: Water Science and Technology Library; Artificial Neural Networks in Hydrology
Uitgeverij: Springer; Springer Netherland
332 Bladzijden
Verschijningsjaar: 2010-12-07
Dordrecht; NL
Gedrukt / Gemaakt in
Gewicht: 0,606 kg
Taal: Engels
199,98 € (DE)
205,59 € (AT)
220,50 CHF (CH)
POD

BC; Previously published in hardcover; Hardcover, Softcover / Geowissenschaften/Sonstiges; Hydrologie und die Hydrosphäre; Verstehen; artificial neural network; groundwater; hydrology; learning; linear optimization; modeling; network; neural networks; B; Hydrogeology; Complex Systems; Theory of Computation; Statistical Physics and Dynamical Systems; Geology; Complex Systems; Theory of Computation; Theoretical, Mathematical and Computational Physics; Earth and Environmental Science; Statistische Physik; Dynamik und Statik; Informatik; Theoretische Informatik; Statistische Physik; BB

Acknowledgements. List of Contributors. Introduction; R.S. Govindaraju, A. Ramachandra Rao. 1. Effective and Efficient Modeling for Streamflow Forecasting; H.V. Gupta, et al. 2. Streamflow Forecasting Based on Artificial Neural Networks; J.D. Salas, et al. 3. Real Time Forecasting Using Neural Networks; M.C. Deo, K. Thirumalaiah. 4. Modular Neural Networks for Watershed Runoff; B. Zhang, R.S. Govindaraju. 5. Radial-Basis Function Networks; R.S. Govindaraju, B. Zhang. 6. Artificial Neural Networks in Subsurface Characterization; D.M. Rizzo, D.E. Dougherty. 7. Optimal Groundwater Remediation Using Artificial Neural Networks; L.L. Rogers, et al. 8. Adaptive Neural Networks in Regulation of River Flows; J.M. Reddy, B.M. Wilamowski. 9. Identification of Pollution Sources Via Neural Networks; G.M. Brion, S. Lingireddy. 10. Spatial Organization and Characterization of Soil Physical Properties Using Self-Organizing Maps; S. Islam, R. Kothari. 11. Rainfall Estimation From Satellite Imagery; K.-L. Hsu, et al. 12. Streamflow Data Infilling Techniques Based on Concepts of Groups and Neural Networks; U.S. Panu, et al. 13. Spatial Analysis of Hydrologic and Environmental Data Based on Artificial Neural Networks; H.-S. Shin, J.D. Salas. 14. Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges; H.R. Maier, G.C. Dandy. 15. Long Range Precipitation Prediction in California: A Look Inside the `Black Box' of a Trained Network; D. Silverman, J.A. Dracup.

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