HR: 09:00h
AN: SF11A-05    [Abstracts]
TI: Data Mining to Improve Management and Reduce Costs Associated With Environmental Remediation
AU: * Minsker, B S
EM: minsker@uiuc.edu
AF: The University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, 3230 Newmark Lab, MC-250, 205 N. Mathews Ave., Urbana, IL 61801 United States
AU: Farrell, D M
EM: d.m.farrell@gmail.com
AF: The University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, 3230 Newmark Lab, MC-250, 205 N. Mathews Ave., Urbana, IL 61801 United States
AB: In this study, data from 105 soil and groundwater remediation projects at BP gas stations were mined for lessons to reduce cost and improve management of remediation sites. A data mining tool called D2K was used to train decision tree, stepwise linear regression and instance based weighting models that relate hydrogeologic, sociopolitical, temporal and remedial factors in the site closure reports to remediation cost. The most important factors influencing cost were found to be the amount of soil excavated and the number of wells installed, suggesting that better management of excavation and well placement could result in significant cost savings. The best model for predicting cost classes (low, medium, and high cost) was the decision tree which had a prediction accuracy of approximately 73%. The misclassification of approximately 27% of the sites in even the best model suggests that remediation costs at service stations are influenced by other site-specific factors that may be difficult to accurately predict in advance.
DE: 9800 GENERAL OR MISCELLANEOUS
DE: 6344 System operation and management
SC: Special Focus: Advances in Data Acquisition, Management, Analysis and Display [SF]
MN: 2004 AGU Fall Meeting