The ubiquitous WebGIS Analysis Toolkit for Extensive Resources (uWATER version 1.1), released on 11/01/2010, is an ArcGIS Explorer plug-in package developed by the Illinois State Water Survey for visualizing and analyzing decision support variables, such as groundwater modeling results, in ArcGIS Explorer build 1500 or later versions (free download). The toolkit conducts a complex inquiry, undertaking spatial and characteristics inquiry at the same time, on a user-desired shapefile based on the defined criteria. It allows the user to select a feature in ArcGIS Explorer from the feature layer. Then it allows the user to select another feature layer or attribute of another feature layer as query criteria, and specifies the attribute query range and spatial relationship.
The uWATER–Pumping Assessment (uWATER-PA) is an ArcGIS Explorer plug-in package developed by the Illinois State Water Survey in ArcGIS Explorer Desktop: build 1500 and 1700 versions (free download). This toolkit is an extended uWATER package targeting the specific environmental issue of groundwater pumping impacts. The uWATER-PA package is an excellent alternative to initially assessing complex groundwater pumping impacts before investing significant time, labor, and funds in monitoring and detailed scientific study. It incorporates simulation of the physics of groundwater flow and user interaction into GIS software. A graphical user interface makes both data entry and interpretation of results intuitive to non-technical individuals. Results are presented as colored maps showing well drawdown (change in groundwater level), and these results can be saved in GIS format for future reference.
We present a framework for accurate estimation of geospatial models from sparse field measurements using image processing and machine learning. The motivation for our work is driven by the cost of field measurements and by the limitations of currently available physics-based modeling techniques. The goal is to improve our understanding of the underlying physical phenomena and increase the accuracy of geospatial models. Our approach is to interpolate sparse field measurements, apply existing physics-based models, incorporate spatial constraints using image processing techniques, explore utilizing auxiliary raster measurements using machine learning, and perform optimization of all algorithmic parameters in supervised, as well as, in unsupervised manner. Our work led to a prototype solution called Spatial Pattern To Learn (SP2Learn) that is available for downloading at http://isda.ncsa.uiuc.edu/download. SP2Learn allows users to explore the accuracy improvements when several image de-noising techniques with a decision tree machine learning technique are employed, and multiple remote sensing and terrestrial raster measurements are used.
PRO-GRADE is a Geographic Information System (GIS) plug-in tool package for recognizing patterns from raster data, such as groundwater recharge and discharge patterns, in ArcGIS 9.2-SP2 or 9.3X. The package consists of two separate programs: (1) the Pattern Recognition Organizer for GIS (PRO-GIS), and (2) the Groundwater Recharge and Discharge Estimator for GIS (GRADE-GIS). PRO-GIS is a general utility that organizes several image processing algorithms into one user interface to offer the flexibility to extract spatial patterns according to the user's needs. It provides generic pattern recognition functions that support virtually any Spatial Decision Support Systems (SDSS) used to assist in management applications such as water resources, land use and agricultural development. GRADE-GIS is a groundwater recharge and discharge estimation interface that requires only hydraulic conductivity, water table and bedrock elevation data for two-dimensional steady state aquifers based on mass balance approaches (Stoertz and Bradbury,1989; Lin and Anderson, 2003). PRO-GRADE is available for downloading at: http://www.sws.uiuc.edu/gws/sware/prograde/