This Small Business Innovation Research (SBIR) Phase I project leads to increased productivity at environmental restoration sites through an innovative integrated application of geographic information systems (GIS), databases, simulation modeling, optimization, and artificial neural networks. The project begins with the understanding that environmental decisions involve many stakeholders, each with different priorities among several objectives. The research goals for this environmental information technology project are to: (1) identify and develop a hierarchy of neural networks that efficiently estimate uncertainty in data and predict the uncertainty as a result of monitoring and remediation decisions; (2) integrate such estimates and data into methods to optimize monitoring and remediation operations, which are coupled with large-scale simulation models used for environmental fate, transport, and risk analysis; (3) store sets of optimized results, which can include different stakeholders' objectives and constraints, in databases; and (4) present results to decision making end-users through a GIS interface. The commercial application of this research, presenting results to decision making end-users through a GIS focuses on subsurface (groundwater and soil) remediation at thousands of sites nationwide, and will be realized by licensing to firms for sales to remediation contractors. The research has near-term applications in climate, weather, air pollution, water, forest, and mineral resources, and emergency planning.
