Adaptive Filters for Water Level Data Processing

Abstract

We describe application of adaptive filters on water level data. The original dataset is a time series of 57,127 measurements and consists of water levels measured at two locations along the St. Jones River, Delaware. After data cleansing to recover for missing data and reduce periodic components in the dataset, adaptive filters are applied to remove the influence of upstream water level on the levels measured downstream.