Effectiveness of Different Artificial Neural Network Training Algorithms in Predicting Protozoa Risks in Surface Waters-Reference-Cited by-同舟云学术

Effectiveness of Different Artificial Neural Network Training Algorithms in Predicting Protozoa Risks in Surface Waters

Published:2002-06 Issue:6 Volume:128 Page:533-542
ISSN:0733-9372
Container-title:Journal of Environmental Engineering
language:en
Short-container-title:J. Environ. Eng.

Author:

Neelakantan T. R.¹,Lingireddy Srinivasa²,Brion Gail M.²

Affiliation:

1. Assistant Professor in Civil Engineering, Shanmugha College of Engineering, Thanjavur 613402, India; formerly, Post Doctoral Research Scholar, Dept. of Civil Engineering, Univ. of Kentucky, Lexington, KY 40506-0281.

2. Associate Professor of Civil Engineering, Univ. of Kentucky, Lexington, KY 40506-0281.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Civil and Structural Engineering,Environmental Chemistry,Environmental Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290733-9372%282002%29128%3A6%28533%29

Reference40 articles.

1. Effect of rainfall on Giardia and Crypto.;Atherholt T. B.;J. Am. Water Works Assoc.,1998

2. Distributed Genetic Algorithm Model on Network of Personal Computers

3. A neural network approach to identifying non-point sources of microbial contamination

4. Using neural networks to predict peak Cryptosporidium concentrations.;Brion G. M.;J. Am. Water Works Assoc.,2001

5. USEPA method 1622.;Clancy J. L.;J. Am. Water Works Assoc.,1999

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