AB: This paper addresses the problem of accurate estimation of geospatial models from a set of groundwater recharge & discharge (R&D) maps and from auxiliary remote sensing and terrestrial raster measurements. The motivation for our work is driven by the cost of field measurements, and by the limitations of currently available physics-based modeling techniques that do not include all relevant variables and allow accurate predictions only at coarse spatial scales. The goal is to improve our understanding of the underlying physical phenomena and increase the accuracy of geospatial models--with a combination of remote sensing, field measurements and physics-based modeling. Our approach is to process a set of R&D maps generated from interpolated sparse field measurements using existing physics-based models, and identify the R&D map that would be the most suitable for extracting a set of rules between the auxiliary variables of interest and the R&D map labels. We implemented this approach by ranking R&D maps using information entropy and mutual information criteria, and then by deriving a set of rules using a machine learning technique, such as the decision tree method. The novelty of our work is in developing a general framework for building geospatial models with the ultimate goal of minimizing cost and maximizing model accuracy. The framework is demonstrated for groundwater R&D rate models but could be applied to other similar studies, for instance, to understanding hypoxia based on physics-based models and remotely sensed variables. Furthermore, our key contribution is in designing a ranking method for R&D maps that allows us to analyze multiple plausible R&D maps with a different number of zones which was not possible in our earlier prototype of the framework called Spatial Pattern to Learn. We will present experimental results using examples R&D and other maps from an area in Wisconsin.
|Original language||English (US)|
|State||Published - 2008|