Artificial Neural Networks in Hydrology

  • R. S. Govindaraju
  • A. Ramachandra Rao

Part of the Water Science and Technology Library book series (WSTL, volume 36)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. R. S. Govindaraju, A. Ramachandra Rao
    Pages 1-5
  3. H. V. Gupta, K. Hsu, S. Sorooshian
    Pages 7-22
  4. J. D. Salas, M. Markus, A. S. Tokar
    Pages 23-51
  5. M. C. Deo, K. Thirumalaiah
    Pages 53-71
  6. Bin Zhang, Rao S. Govindaraju
    Pages 73-91
  7. Rao S. Govindaraju, Bin Zhang
    Pages 93-109
  8. Donna M. Rizzo, David E. Dougherty
    Pages 111-134
  9. L. L. Rogers, V. M. Johnson, F. U. Dowla
    Pages 135-152
  10. J. Mohan Reddy, Bogdan M. Wilamowski
    Pages 153-177
  11. G. M. Brion, S. Lingireddy
    Pages 179-197
  12. K.-L. Hsu, H. V. Gupta, X. Gao, S. Sorooshian
    Pages 209-234
  13. Back Matter
    Pages 331-332

About this book

Introduction

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.

Keywords

artificial neural network groundwater hydrology learning linear optimization modeling network neural networks

Editors and affiliations

  • R. S. Govindaraju
    • 1
  • A. Ramachandra Rao
    • 1
  1. 1.Purdue UniversityWest LafayetteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-015-9341-0
  • Copyright Information Springer Science+Business Media B.V. 2000
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-5421-0
  • Online ISBN 978-94-015-9341-0
  • Series Print ISSN 0921-092X
  • About this book