Relative Performance of Artificial Neural Networks and Regression Models in Predicting Missing Water Quality Data-Reference-Cited by-同舟云学术

Relative Performance of Artificial Neural Networks and Regression Models in Predicting Missing Water Quality Data

Published:2008-06 Issue:5 Volume:25 Page:657-668
ISSN:1092-8758
Container-title:Environmental Engineering Science
language:en
Short-container-title:Environmental Engineering Science

Author:

Tyagi Punam¹,Chandramouli V.²,Lingireddy Srinivasa³,Buddhi D.⁴

Affiliation:

1. Biosystems and Agricultural Engineering Department, College of Agriculture, University of Kentucky, Lexington, KY 40546.

2. Department of Civil Engineering, College of Agriculture, University of Kentucky, Lexington, KY 40546.

3. Department of Civil Engineering, College of Agriculture, University of Kentucky, Lexington, KY 40546. Phone: 859-257-5243; Fax: 859-257-5243; E-mail: punam.tyagi@uky.edu

4. School of Energy & Environmental Studies, Devi Ahilya University, Indore, M.P. 452017, India.

Publisher

Mary Ann Liebert Inc

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry

Link

http://www.liebertpub.com/doi/pdf/10.1089/ees.2007.0045

Reference38 articles.

1. Drinking water quality and treatment: the use of artificial neural networks

2. Experience in industrial plant model development using large-scale artificial neural networks

3. A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain

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