Application of Data Fusion and Knowledge Extraction in Hydrogeology

Abstract:

Since the development of personal computers, the modeling of groundwater systems shifted from analytical equations to numerical models. Given the ill-posed nature and non-uniqueness of numerical groundwater models, the use of alternate data fusion and knowledge extraction paradigms is being explored to reduce uncertainty through improvements in the conceptualization and parameterization processes and boundary conditions. This presentation demonstrates the use of data fusion using joint-inverse, artificial adaptive system, and hybrid modeling techniques to assist with these challenges. Examples include using joint-inversion for coupled unsaturated zone and geothermal studies, using artificial adaptive systems in water-quality and groundwater recharge studies including subset selection, using hybrid solutions for remote mapping of surficial aquifers and landscape characteristics, and forecasting climate change.