The application of selected statistical tests in the detection and removal of outliers in water engineering data based on the example of piezometric measurements at the Dobczyce dam over the period 2012-2016

Abstract

Due to their size, water construction belongs to the largest and heaviest engineering structures. Ensuring the safe operation of such facilities requires continuous monitoring. Among the basic forms of monitoring in such facilities one should list continuous seasonal piezometric measurements, which are obligatory elements of general control measurements aimed at ensuring safety when using the facility. The latter is directly related to guaranteeing the safety of people living and working in an area exposed to destruction in the event of a possible disaster involving the building. From the perspective of increasing the safety of the hydrotechnical facility, optimal conditions occur when the water levels in piezometers oscillate around a constant value as this signalizes that filtration processes in the body and the surface of a dam are stable. Various factors may disturb measurements of water levels in open piezometers or water pressure in closed piezometers. These factors may take the form of systematic, random or obvious errors. Thus, before analyzing this type of data, the largest errors (outliers) should be removed from the sample as they could significantly affect the outcomes of analysis and lead to a false interpretation. In such a situation, it is necessary to apply respective statistical tests, which allow verification of whether a particular portion of data may be treated as a set of outliers at a given significance level α. In this work the following statistical tests were used to identify and remove outliers: Q-Dixon test, Grubbs test, Hampel test, Rosner test, Iglewicz and Hoaglin test, Tietjen-Moore test and quartile test. The scope of the empirical analysis is focused on piezometric measurements at the Dobczyce dam over the period 2012-2016.