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Data-mining Based Detection of Glaciers: Quantifying the Extent of Alpine Valley Glaciation

Kory J. Allred Wei Luo

*Corresponding author: Kory J. Allred kallred@niu.edu

geosciences2015,1,1doi:10.3934/geosci.2015.1.1

The extent of glaciation in alpine valleys often gives clues to past climates, plate movement, mountain landforms, bedrock geology and more. However, without field investigation, the degree to which a valley was affected by a glacier has been difficult to assess. We developed a model that uses quantitative parameters derived from digital elevations model (DEM) data to predict whether a glacier was likely present in an alpine valley. The model's inputs are mainly derived from the basin hypsometry, and a new parameter termed the Hypothetical Basin Equilibrium Elevation (HBEE), which is based on the equilibrium elevation altitude (ELA) of a glacier. We used data mining techniques that comb through large data sets to find patterns for classification and prediction as the basis for the model. Four classifiers were utilized, and each was tested with two different training set/test data ratios of nearly 150 basins that were previously delineated as fully- or non-glaciated. The classifiers had a predictive accuracy of up to 90% with none falling below 72%. Two of the classifiers, classification tree and naïve-Bayes, have graphical outputs that visually describe the classification process, predictive results, and in the naïve-Bayes case, the relative effectiveness towards the model of each attribute. In all scenarios, the HBEE was found to be an accurate predictor for the model. The model can be applied to any area where glaciation may have occurred, but is particularly useful in areas where the valley is inaccessible for detailed field investigation.

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