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Title: Reducing uncertainty and improving precision in coincident geospatial datasets using weight-of-evidence : part 1
Authors: Cegan, Jeffrey C.
Wood, Matthew D.
Linkov, Igor.
Loney, Drew A.
Keywords: Geospatial data
Data sets
Publisher: Coastal and Hydraulics Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Note (Engineer Research and Development Center (U.S.)) ; no. ERDC/CHL CHETN-XII-1
Abstract: This U.S. Army Engineer Research and Development Center (ERDC) Technical Note (TN) is the first of multiple TNs focused on improving environmental datasets in limited-knowledge conditions by merging multiple datasets, each with high uncertainty and low precision, together with institutionalized subject matter expert knowledge to increase accuracy and precision. This TN provides a brief overview of geospatial data fusion and uncertainty quantification for environmental datasets. Additionally, this TN details the progress and current results following an investigation of the working hypothesis that a weight-of-evidence (WOE) framework that joins qualitative and quantitative datasets can significantly improve the accuracy and precision as related to individual datasets and current data fusion algorithms.
Description: Technical Note
Rights: Approved for Public Release; Distribution is Unlimited
Size: 14 pages / 1.593 Mb
Types of Materials: PDF
Appears in Collections:Technical Note

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