Use of Pattern Recognition to Optimize Site Investigation
Small Business Information
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AbstractNOVASOL is pleased to propose development of pattern recognition technology in the analysis of data related to remedial investigations at Dense Non-Aqueous Phase Liquid (DNAPL) contaminated sites. We have named our concept Pattern Recognition OptimizationTechnology for Investigation of Contaminated Sites (PROTICS). The proposed PROTICS system will consist of geostatistical modeling, spatial visualization modeling, pattern recognition modeling and geographic data integration to analyze diverse data types,identify data gaps or insignificant data types, and provide a useful tool to identify potential DNAPL sources in the subsurface. Although these modeling approaches have previously been used individually in many environmental applications, combining themtogether into one systematic analysis tool will overcome the limitations of each individual model to establish spatial relationships among the different data types. This data integration and digestion system will optimize the use of the enormous amountsof information that have been collected for many Department of Defense (DoD) sites. The spatial data analysis and statistical modeling results produced from the proposed research will enable more precise delineation of DNAPL contaminant plumes. The PROTICS system that we are proposing in this SBIR embodies features that will yield a unique competitive advantage in the marketplace. There are a significant number of DNAPL contaminated sites and other complex remedial investigation site worldwideat both DoD and civilian locations. A great need exists for a systematic approach to intelligently digest the large quantities of data that remedial investigation of these sites produces. The advanced pattern recognition and modeling capability of theproposed PROTICS will allow improved site characterization, thereby aiding the development of effective remedies and reducing remediation costs. Therefore, a great demand for such a system can be anticipated.
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