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.
Artificial Neural Networks in Hydrology
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Editorial Reviews
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`The book can be recommended as an important (but not the only) source of information on applications of artificial neural networks to hydrological problems. It would be useful for graduate students, researchers and hyrologists.' World Meteorological Organization Bulletin, 50:3 (2001)
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Artificial Neural Networks are now widely applied in a broad range of fields, including image processing, signal processing, medical studies, financial predictions, power systems, and pattern recognition among others (Kosko, 1992; Refenes et al., 1994; Saund, 1989; Schalkoff, 1992; Suykens et. al. 1996; Vemuri and Rogers 1994). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
infilling techniques, virus decay rate, encysted protozoa, streamflow forecasting, river flow prediction, forecasting using neural networks, gating network, summation layer, input training vectors, posterior probability maps, snow water equivalent, rainfall estimation, missing data values, normalized window, counterpropagation networks, precipitation estimation, rainfall forecasting, calculation pixel, input lags, hydrologic applications, winner node, runoff values, kriging weights, streamflow data, competitive layer Key Phrases - Capitalized Phrases (CAPs): (learn more)
Water Resources Research, Murray Bridge, English River, Kluwer Academic Publishers, Ramachandra Rao, River Murray, Environmental Engineering, Council Grove, San Francisco, South Korea, Halfway River, Journal of Hydrology, Little Patuxent River, Academic Press, John Wiley, Los Pinos River, National Weather Service, Whalen Diversion, Lawrence Livermore National Laboratory, Sioux Lookout, South Australia, Hydraulic Engineering, Japanese Islands, San Mateo, Jacobson Agricultural New!
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Artificial Neural Networks are now widely applied in a broad range of fields, including image processing, signal processing, medical studies, financial predictions, power systems, and pattern recognition among others (Kosko, 1992; Refenes et al., 1994; Saund, 1989; Schalkoff, 1992; Suykens et. al. 1996; Vemuri and Rogers 1994). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
infilling techniques, virus decay rate, encysted protozoa, streamflow forecasting, river flow prediction, forecasting using neural networks, gating network, summation layer, input training vectors, posterior probability maps, snow water equivalent, rainfall estimation, missing data values, normalized window, counterpropagation networks, precipitation estimation, rainfall forecasting, calculation pixel, input lags, hydrologic applications, winner node, runoff values, kriging weights, streamflow data, competitive layer Key Phrases - Capitalized Phrases (CAPs): (learn more)
Water Resources Research, Murray Bridge, English River, Kluwer Academic Publishers, Ramachandra Rao, River Murray, Environmental Engineering, Council Grove, San Francisco, South Korea, Halfway River, Journal of Hydrology, Little Patuxent River, Academic Press, John Wiley, Los Pinos River, National Weather Service, Whalen Diversion, Lawrence Livermore National Laboratory, Sioux Lookout, South Australia, Hydraulic Engineering, Japanese Islands, San Mateo, Jacobson Agricultural New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
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- Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon Haykin on 4 pages
- Civil Engineering: Objective Type for Degree Classes and for G.A.T.E. Page II by Jaya Rami Reddy in Front Matter, and page 153
- Artificial Neural Networks by Robert J. Schalkoff on page 7, and page 22
- Time Series Analysis: Forecasting & Control (3rd Edition) by George Box on page 256, and page 307
- Entropy and Energy Dissipation in Water Resources (Water Science and Technology Library) by V.P. Singh on page 257, and Back Matter
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- Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling by Praveen Kumar on page 433, and page 434
- Watershed Models by Vijay P. Singh on page 15
- Ecological Informatics by Friedrich Recknagel on page 232
- Riverbank Filtration: Improving Source-Water Quality (Water Science and Technology Library) by C. Ray in Back Matter
- Computational Intelligence in Control by Ruhul Amin on page 216
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