A comparative study: Prediction of constructed treatment wetland performance with k-nearest neighbors and neural networks

Lee, B-H and Scholz, M ORCID: https://orcid.org/0000-0001-8919-3838 2006, 'A comparative study: Prediction of constructed treatment wetland performance with k-nearest neighbors and neural networks' , Water, Air, and Soil Pollution, 174 (1-4) , pp. 279-301.

Full text not available from this repository. (Request a copy)

Abstract

K-nearest neighbors (KNN), support vector machine (SVM) and self-organizing map (SOM) were applied to predict five-day @ 20˚C N- Allylthiourea biochemical oxygen demand (BOD) and suspended solids (SS), and to assess novel alternative methods of analyzing water quality performance indicators for constructed treatment wetlands. Concerning the accuracy of prediction, SOM showed a better performance compared to both KNN and SVM. Moreover, SOM had the potential to visualize the relationship between complex biochemical variables. However, optimizing the SOM requires more time in comparison to KNN and SVM because of its trial and error process in searching for the optimal map. The results suggest that BOD and SS can be efficiently estimated by applying machine learning tools with input variables such as redox potential and conductivity, which can be monitored in real time. Their performances are encouraging and support the potential for future use of these models as management tools for the day-to-day process control.

Item Type:	Article
Themes:	Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title:	Water, Air, and Soil Pollution
Publisher:	Springer
Refereed:	Yes
ISSN:	0049-6979
Depositing User:	Users 29196 not found.
Date Deposited:	31 May 2012 12:56
Last Modified:	27 Mar 2019 06:05
URI:	http://usir.salford.ac.uk/id/eprint/22805

Actions (login required)

Edit record (repository staff only)