Abstract
The water engineering safety monitoring data inevitably existed outliers, but the most widely used least squares method (LS) had no ability to remove the outliers. And the LS was more easily disturbed by the outliers to mistake the actual regression curves. Aiming at the defects of LS, the least trimmed squares (LTS) was introduced, based on the least minimizing residual sum of squares, to construct the water engineering safety monitoring model. According to and analyzing monitoring data of actual project, the optimal data groups were obtained by removing the outliers. And then the most practical fitting curve was obtained by getting the regression coefficient of the optimal data groups. Compared with the least square method, the LTS results were more reasonable and robust. Meanwhile, the prediction accuracy of the data was significantly improved. Therefore, the LTS was great prospect for the water engineering safety monitoring data analysis.
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