PATTERN RECOGNITION PRINCIPLES TOU GONZALEZ PDF

A spatially explicit, slope-based algorithm was created to delineate MFR zones in 17 arid, mountainous watersheds using elevation and land cover data. Slopes were calculated from elevation data and grouped into classes using iterative self-organizing classification. Land cover types that were inconsistent with groundwater recharge were excluded from the candidate areas to determine the final MFR zones. Slopes and surficial geologic materials that were present in the MFR zones were consistent with conditions at the mountain front, while soils and land cover that were present would generally promote groundwater recharge. Visual inspection of the MFR zone maps also confirmed the presence of well-recognized alluvial fan features in several study watersheds. While qualitative evaluation suggested that the algorithm reliably delineated MFR zones in most watersheds overall, the algorithm was better suited for application in watersheds that had characteristic Basin and Range topography and relatively flat basin floors than areas without these characteristics.

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Before electronic computers became available in the fifties , natural pattern recognition capabilities of animals and humans could be tested in psychophysical experiments, but artificial pattern recognition by machines was beyond the state of the art.

This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Preview Unable to display preview. Download preview PDF. Bibliographical Notes M. Anderberg: Cluster Analysis for Applications. Academic Press, New York Google Scholar H. Wiley-Interscience, New York Google Scholar M. Bongard: Pattern Recognition. Spartan Books, New York-Washington Translated from Russian. Google Scholar R.

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Pattern recognition principles

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