Dataset: Efficient improvement for water quality analysis with large amount of missing data

Published: 26 July 2022| Version 1 | DOI: 10.17632/8y42cbc7h8.1
David Sierra Porta


Water is vital for life and local water pollution can damage the environment and affect human health. Governments and private institutions monitor and regulate water quality to protect the environment and populations. The consequences of pollution can reach far and wide, costing companies significant amounts in cleanup costs and loss of reputation. Most countries have official accredited laboratories and sampling teams that use varied technology, global expertise and local knowledge to provide water quality monitoring for different types of water and different and varied sampling locations. However, one of the main problems associated with monitoring and assessing water quality and meeting minimum standards of potability or usability is the analysis of samples based on local data. The problem lies in the fact that in many cases the data, due to the methodology or technique used or the expertise of the human resource that handles the samples, ends up configured in sets that have a large amount of missing information or data without information. This implies a problem depending on the analysis to be carried out. If you want to estimate a water quality index based on the samples, then you may have biased calculations due to the loss of information. This dataset has been used for the generation of the manuscript: Efficient improvement for water quality analysis with large amount of missing data. D. Sierra-Porta,M. Tobón-Ospino. This manuscript is being submitted to Sustainable Production and Consumption (2022 Elsevier), Publication of the Institution of Chemical Engineers.



Universidad Tecnologica de Bolivar


Machine Learning, Water Quality, Advanced Multivariate Statistics, Missing Data Analysis, Drinking Water Quality