Various filtering algorithms used to eliminate outliers in velocity time series obtained by ADVs (acoustic Doppler velocimeter)

The correct measurement of velocities in rivers is important for the true determination of discharge, erosion, scouring, and sediment transport processes. With the goal of increased accuracy, the use of acoustic Doppler velocimeters (ADVs) is increasing in hydrological measurements in rivers, lakes, and laboratories. ADVs are extensively used in the USA. ADVs have advantages when compared with classical measurement devices; however, one must be careful while using an ADV because their sampling approach creates a large number of extreme values by sending signals into the flow, measuring the velocities of particles moving with the water and assuming that these particles move with same velocity as the water. To calculate unbiased statistical properties, outliers must be removed from the time series. This study explains the methods used to filter velocity time series collected with ADVs and investigates the effects of these filters on the statistical characteristics of the filtered time series.