Environmental site characterization via artificial neural network approach

This study explored the potential use of ANNs for profiling and characterization of various environmental sites. A static ANN with back-propagation algorithm was used to model the environmental containment at a hypothetical data-rich contaminated site. The performance of the ANN profiling model was then compared with eight known profiling methods. The comparison showed that the ANN-based models proved to yield the lowest error values in the 2-D and 3-D comparison cases. The ANN-based profiling models also produced the best contaminant distribution contour maps when compared to the actual maps. Along with the fact that ANN is the only profiling methodology that allows for efficient 3-D profiling, this study clearly demonstrates that ANN-based methodology, when properly used, has the potential to provide the most accurate predictions and site profiling contour maps for a contaminated site. ANN with a back-propagation learning algorithm was utilized in the site characterization of contaminants at the Kansas City landfill. The use of ANN profiling models made it possible to obtain reliable predictions about the location and concentration of lead and copper contamination at the associated Kansas City landfill site. The resulting profiles can be used to determine additional sampling locations, if needed, for both groundwater and soil in any contaminated zones. Back-propagation networks were also used to characterize the MMR Demo 1 site. The purpose of the developed ANN models was to predict the concentrations of perchlorate at the MMR from appropriate input parameters. To determine the most-appropriate input parameters for this model, three different cases were investigated using nine potential input parameters. The ANN modeling used in this case demonstrates the neural network’s ability to accurately predict perchlorate contamination using multiple variables. When comparing the trends observed using the ANN-generated data and the actual trends identified in the MMR 2006 System Performance Monitoring Report, both agree that perchlorate levels are decreasing due to the use of the Extraction, Treatment, and Recharge (ETR) systems. This research demonstrates the advantages of ANN site characterization modeling in contrast with traditional modeling schemes. Accordingly, characterization task-related uncertainties of site contaminations were curtailed by the use of ANN-based models.

Grantor dc:publisher

Kansas State University

Year dc:date.issued

2008

Author dc:creator

• Mryyan, Mahmoud

• Artificial neural network
• Site characterization
• Environmental

Statement dc:rights

• © the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/2097/1120

OAI identifier oai:identifier

oai:krex.k-state.edu:2097/1120