Journal of Hydroinformatics In Press, Uncorrected Proof © IWA Publishing 2010 | doi:10.2166/hydro.2010.187
A framework for accurate geospatial modeling using image ranking and machine learning
Peter Bajcsy, Yu-Feng Lin, Alex Yahja and Chulyun Kim
Illinois State Water Survey, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Software Design and Management, Kyungwon University, Gyeonggi-Do, 461-701, Korea
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W. Clark, Urbana, IL 61801, USA. Tel.: +1 217 265 5387. E-mail: email@example.com
First received 9 October 2009; accepted in revised form 22 January 2010. Available online 4 October 2010.
There is a large class of modeling problems where the complexity of the underlying phenomena is overwhelming and hence the accuracy of mathematical models is limited. Our approach to this class of problems is to design frameworks that bring together physically based and data-driven models, and incorporate the tacit knowledge of experts by providing visual exploration and feedback capabilities. This paper presents such a novel computer-assisted framework for accurate geospatial modeling applied to improve groundwater recharge and discharge (R/D) patterns. The novelty of our work is in designing a methodology for ranking and extracting relationships, as well as in developing a general framework for building accurate geospatial models. The framework combines variables derived using physically based inverse modeling with auxiliary geospatial variables directly sensed, ranks variables and extracts variable relationships using data-driven (“machine learning”) techniques, and supports partially expert-driven trial-and-error experimentation and more rigorous optimization, as well as visual explorations, to derive more accurate models for R/D pattern estimation. When the framework was tested by experts, it led to a high level of consistency between the machine-learning-based knowledge and the experts’ knowledge about R/D distribution. The prototype solution of the framework is available for downloading at http://isda.ncsa.uiuc.edu/Sp2Learn/.
Keywords: groundwater modeling, machine learning, optimization, pattern recognition
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