Landfill remediation via decision analysis

The objective of the project has been to develop software that will aid in choosing the optimum remediation strategy for polluting landfills. The key parts of the work have been: developing a methodologies and models for reliability and uncertainty within a remedial action (e.g., caps and hydraulic parameters); developing artificial neural networks as a supplement to numerical models for groundwater and contaminant transport; and applying decision analysis techniques to combinatorial optimization results. The main accomplishments in 1999 were investigating alternative decision-maker types' influence on optimum strategy and investigating data mining techniques as a method to garner new information from existing databases. Looking at the results for 1999, one sees a refinement of the post-processing and analysis of the results from the inclusion of uncertainty, cost, reliability and other objectives into the design optimization process. Analysis of different decision-maker positions can be quantified, and this can enhance transparency in analysis of the optimization results. Lastly, data mining techniques have shown worth as a means to garner new knowledge from existing, and supposedly well studied data sets.
Ph.D. and post doc study by Thomas Donald Krom funded by ATV, Elsamprojekt A/S and Groundwater Research Centre.