Univariate imputation method for recovering missing data in wastewater treatment process

Abstract

• A univariate imputation method, namely UIM, is proposed for WWTP time series. • The employ STL is the best choice for decomposing the WWTP data. • A self-similarity decomposition is designed to detect the repeating patterns. • Extensive experimental results verify the effectiveness of UIM. High-quality data play a paramount role in monitoring, control, and prediction of wastewater treatment process (WWTP) and can effectively ensure the efficient and stable operation of system. Missing values seriously degrade the accuracy, reliability and completeness of the data quality due to network collapses, connection errors and data transformation failures. In these cases, it is infeasible to recover missing data depending on the correlation with other variables. To tackle this issue, a univariate imputation method (UIM) is proposed for WWTP integrating decomposition method and imputation algorithms. First, the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal, trend and remainder components to deal with the nonstationary characteristics of WWTP data. Second, the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values. A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern. Third, all the imputed results are merged to obtain the imputation result. Finally, six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators. The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios. Therefore, the proposed UIM is a promising method to impute WWTP time series.