Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Statistical parameters</td><td class="align_center">PL11-3</td><td class="align_center">PL11-4</td><td class="align_center">PL13-3</td><td class="align_center">PL13-4</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Mean value</td><td class="align_center">0.19</td><td class="align_center">0.19</td><td class="align_center">0.19</td><td class="align_center">0.20</td></tr><tr><td class="align_left">Standard deviation</td><td class="align_center">0.88</td><td class="align_center">0.64</td><td class="align_center">0.94</td><td class="align_center">0.71</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Statistical parameters of deformation data.</div>

Advances in Civil Engineering

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Table 3

Table 3: Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data 