Journal article
Neural networks provide superior description of Giardia lamblia inactivation by free chlorine
Water research (Oxford), v 38(14), pp 3449-3457
2004
PMID: 15276762
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Use of conventional models to describe data on microbial inactivation during disinfection suffers from limitations with respect to flexibility and direct quantitative incorporation of water quality variables. This paper develops an approach to analysis of such data using neural networks (NNs). Using the data on free chlorine inactivation of
Giardia lamblia previously reported, it was found that the use of an NN with a single hidden layer and four hidden neurons provided a superior (better) fit to the data with a reduced number of model parameters when compared to the fitting of this data using a conventional approach. Therefore, the use of NN models should be considered in the future assessment of microbial inactivation during disinfection. Incorporation of additional facets of the disinfection process, such a disinfectant decay, needs to be considered in subsequent development of this approach.
Metrics
Details
- Title
- Neural networks provide superior description of Giardia lamblia inactivation by free chlorine
- Creators
- Charles N Haas - Drexel University, 33rd and Market Streets, Philadelphia, PA 19104, USA
- Publication Details
- Water research (Oxford), v 38(14), pp 3449-3457
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000223414000034
- Scopus ID
- 2-s2.0-3342891563
- Other Identifier
- 991014878010704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Environmental
- Environmental Sciences
- Water Resources