Missing data in water quality time series lead to some generał problems in many fields of environmental research and simulation. They cause not only difficulties in process identification and parameter estimation but also misinterpretations of spatial and temporal variations of water quality indicators. Mostly, time series represent samples of data at discrete time events based on various sampling intervals. For modelling and simulation of water quality processes time series must be mapped on a regular time grid. This procedure is known as re-sampling of time series and consists on data interpolation or, in the case of disturbed signals, on data approximation. Some well-known linear and nonlinear interpolation methods exist white data approximation can be done by static and dynamie procedures. Regression type functions or in the case of cycling time series Fourier approximations are mainly used. By these procedures equidistant data will be obtained. In opposite of that, digital filtering procedures deliver consistent equidistant data estimates based on major signal frequencies. In the paper different algorithms of data interpolation and approximation are applied on irregularly sampled water quality time of rivers with different hydraulic conditions. Additionally, low pass filters are checked to find out the best filter function for each water quality indicator.
Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.
Oct 15, 2021
Jul 19, 2021