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/.
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Research Article|
October 04 2010
A framework for accurate geospatial modeling using image ranking and machine learning
Peter Bajcsy;
1National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W. Clark, Urbana, IL 61801, USA
E-mail: [email protected]
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Yu-Feng Lin;
Yu-Feng Lin
2Illinois State Water Survey, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Alex Yahja;
Alex Yahja
1National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W. Clark, Urbana, IL 61801, USA
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Chulyun Kim
Chulyun Kim
3Department of Software Design and Management, Kyungwon University, Gyeonggi-Do, 461-701, Korea
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Journal of Hydroinformatics (2011) 13 (3): 443–460.
Article history
Received:
October 09 2009
Accepted:
January 22 2010
Citation
Peter Bajcsy, Yu-Feng Lin, Alex Yahja, Chulyun Kim; A framework for accurate geospatial modeling using image ranking and machine learning. Journal of Hydroinformatics 1 July 2011; 13 (3): 443–460. doi: https://doi.org/10.2166/hydro.2010.187
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