| Artificial Neural Networks in Hydrology 2000 Edition Contributor(s): Govindaraju, R. S. (Editor), Rao, A. R. (Editor) |
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ISBN: 0792362268 ISBN-13: 9780792362265 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: May 2000 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Neural Networks - Medical - Science | Earth Sciences - Hydrology |
| Dewey: 551.480 |
| LCCN: 00028203 |
| Series: Water Science and Technology Library |
| Physical Information: 0.81" H x 6.14" W x 9.21" (1.48 lbs) 332 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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 (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy. |