ISWS - Center for Groundwater Science - Illinois Open Source License Agreement

Illinois State Water Survey
Address / Phone Information

Software Development

PRO-GRADE
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 or later versions. 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/

SP2Learn
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.