Osman, Muhammad SAbu-Mahfouz, Adnan MIPage, Philip R2018-11-282018-11-282018-11Osman, M.S., Abu-Mahfouz, A.M.I. and Page, P.R. 2018. A survey on data imputation techniques: Water distribution system as a use case. IEEE Access: DOI: 10.1109/ACCESS.2018.28772692169-3536http://hdl.handle.net/10204/10547Open access article published on IEEE Access: DOI: 10.1109/ACCESS.2018.2877269The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a "top-down bottom-up'' two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs.enData imputationDeletionMachine-learning methodsMissing dataModel based proceduresMultiple imputationSingle imputationWater distribution systemsArticleOsman, M. S., Abu-Mahfouz, A. M., & Page, P. R. (2018). A survey on data imputation techniques: Water distribution system as a use case. http://hdl.handle.net/10204/10547Osman, Muhammad S, Adnan MI Abu-Mahfouz, and Philip R Page "A survey on data imputation techniques: Water distribution system as a use case." (2018) http://hdl.handle.net/10204/10547Osman MS, Abu-Mahfouz AM, Page PR. A survey on data imputation techniques: Water distribution system as a use case. 2018; http://hdl.handle.net/10204/10547.TY - Article AU - Osman, Muhammad S AU - Abu-Mahfouz, Adnan MI AU - Page, Philip R AB - The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a "top-down bottom-up'' two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs. DA - 2018-11 DB - ResearchSpace DP - CSIR KW - Data imputation KW - Deletion KW - Machine-learning methods KW - Missing data KW - Model based procedures KW - Multiple imputation KW - Single imputation KW - Water distribution systems LK - https://researchspace.csir.co.za PY - 2018 SM - 2169-3536 T1 - A survey on data imputation techniques: Water distribution system as a use case TI - A survey on data imputation techniques: Water distribution system as a use case UR - http://hdl.handle.net/10204/10547 ER -