Utilization of Basic Methods for Filling Missing Data from Wastewater Treatment Plants

The article examines the possibilities for big data analysis, focusing on dealing with missing data. By exploring the techniques and methodologies used for imputation, interpretation, and modeling of missing data in the context of big data analytics, we emphasize the relationship between data completeness and the quality of solutions. The ability to effectively handle missing data not only enhances the reliability and validity of analytical insights but also enables informed strategic decision-making. An experiment was conducted on filling missing data in a dataset based on three different mathematical models. The best result is reported with the autoregressive recursive interpolation model. For this purpose, data from a wastewater treatment plant, which have not been the subject of research so far, were used.