Abstract
Advances in technology are allowing scientists and engineers to perform their jobs in ways that are both more time-efficient and cost-effective. With the number of abandoned landfills increasing every year, it is essential that the cost of investigation and remediation of such sites remain as low as possible. To achieve this objective, many site investigator teams are choosing to employ the powerful function approximation feature of artificial neural networks (ANN). Once the desired ANN profiling system is trained on existing data, the system can be used to produce efficient predictions for the unexplored regions. Similar ANN profiling systems were utilized herein in the investigation of an abandoned landfill site in Kansas City, KS. The developed ANN systems were trained on existing data and then were used to predict the amounts and distribution of arsenic and lead contaminates within the landfill area. The site investigating team was able to capitalize on the information generated via the developed ANN systems in order to determine the strategic locations for further testing. Therefore, less money was spent since no further testing was needed in the relatively uncontaminated regions.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call