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Artificial Neural Networks in Hydrology

  • Book
  • © 2000

Overview

Editors:
  1. R. S. Govindaraju

    1. Purdue University, West Lafayette, USA

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  2. A. Ramachandra Rao

    1. Purdue University, West Lafayette, USA

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

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Introduction

    • R. S. Govindaraju, A. Ramachandra Rao
    Pages 1-5

  3. Effective and Efficient Modeling for Streamflow Forecasting

    • H. V. Gupta, K. Hsu, S. Sorooshian
    Pages 7-22

  4. Streamflow Forecasting Based on Artificial Neural Networks

    • J. D. Salas, M. Markus, A. S. Tokar
    Pages 23-51

  5. Real Time Forecasting Using Neural Networks

    • M. C. Deo, K. Thirumalaiah
    Pages 53-71

  6. Modular Neural Networks for Watershed Runoff

    • Bin Zhang, Rao S. Govindaraju
    Pages 73-91

  7. Radial-Basis Function Networks

    • Rao S. Govindaraju, Bin Zhang
    Pages 93-109

  8. Artificial Neural Networks in Subsurface Characterization

    • Donna M. Rizzo, David E. Dougherty
    Pages 111-134

  9. Optimal Groundwater Remediation Using Artificial Neural Networks

    • L. L. Rogers, V. M. Johnson, F. U. Dowla
    Pages 135-152

  10. Adaptive Neural Networks in Regulation of River Flows

    • J. Mohan Reddy, Bogdan M. Wilamowski
    Pages 153-177

  11. Identification of Pollution Sources Via Neural Networks

    • G. M. Brion, S. Lingireddy
    Pages 179-197

  12. Spatial Organization and Characterization of Soil Physical Properties Using Self-Organizing Maps

    • Shafiqul Islam, Ravi Kothari
    Pages 199-207

  13. Rainfall Estimation from Satellite Imagery

    • K.-L. Hsu, H. V. Gupta, X. Gao, S. Sorooshian
    Pages 209-234

  14. Streamflow Data Infilling Techniques Based on Concepts of Groups and Neural Networks

    • U. S. Panu, M. Khalil, A. Elshorbagy
    Pages 235-258

  15. Spatial Analysis of Hydrologic and Environmental Data Based on Artificial Neural Networks

    • Hyun-Suk Shin, Jose D. Salas
    Pages 259-286

  16. Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges

    • H. R. Maier, G. C. Dandy
    Pages 287-309

  17. Long Range Precipitation Prediction in California: A Look Inside the “Black Box” of a Trained Network

    • David Silverman, John A. Dracup
    Pages 311-329

  18. Back Matter

    Pages 331-332
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Keywords

About this book

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.

Reviews

`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)

Editors and Affiliations

  • Purdue University, West Lafayette, USA

    R. S. Govindaraju, A. Ramachandra Rao

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