Applications of Missing Data Imputation Methods in Wastewater Treatment Plants

Missing data pose a big challenge in the field of wastewater treatment, representing a frequent issue in data quality that can result in misleading analyses and compromised decision-making accuracy. The initial step in data preprocessing involves the estimation and handling of missing values. The primary aim to conduct a comprehensive examination of the existing research concerning missing value imputation in wastewater treatment plants (WWTPs). The focus is specifically on identifying and outlining various imputation techniques employed in this field, while paying close attention to their respective strengths and limitations. To ensure a methodical approach, this study adopts the systematic literature review (SLR) using Kitchenham's guidelines. In order to gather relevant and up-to-date papers, the research leverages the scientific database "Scopus" to retrieve and analyze all pertinent papers during the search process. By doing so, this research aims to contribute valuable insights into the different strategies used for imputing missing values in WWTPs and to shed light on their practical implications and potential drawbacks. Form 599, a total of 16 research papers were selected to assess the review questions. Finally, several recommendations were given to address the limitations identified in the reviewed studies and to contribute to more accurate and reliable data analysis and decision-making in the wastewater treatment domain.