Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Expert Systems to Assist in Evaluation of Measurement Data.
Author Greathouse, D. G. ;
CORP Author Environmental Protection Agency, Cincinnati, OH. Risk Reduction Engineering Lab.
Publisher c1991
Year Published 1991
Report Number EPA/600/D-91/010;
Stock Number PB91-162743
Additional Subjects Environmental tests ; Environmental surveys ; Data processing ; Technology transfer ; Quality control ; Waste disposal ; Decision making ; Geophysics ; Quality assurance ; Reprints ; Expert systems ; Flexible Membrane Liner System(FLEX)
Library Call Number Additional Info Location Last
NTIS  PB91-162743 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 06/13/1991
Collation 7p
Expert systems are computer programs designed to provide advice in a specialized area that is comparable to the advice which would be provided by an expert or knowledgeable person in the area. Development of these systems for a particular application is feasible if expert(s) are available who can perform the evaluation in a reasonable length of time. Evaluation of measurement data that are collected according to standard protocols can usually be performed by experts in the laboratory sciences and application fields with possible assistance from a professional statistician. Expert systems have the potential for improving the productivity of less experienced persons responsible for evaluating or interpreting measurement data. The Risk Reduction Engineering Laboratory has developed an expert system to assist in evaluation of the chemical compatibility of flexible membrane liners based on the data from prescribed physical measurements performed on sample specimens of the liner material. The goal of these tests is to determine if a liner material will be chemically resistant to a leachate from a hazardous waste landfill. The system provides an example of the type of systems that could be developed to improve the quality of decisions based on measurement data. The system will be described and demonstrated (if desired) to illustrate the type of systems that could be developed for other types of measurement data interpretations.