Classification of Water Quality Index Using Machine Learning Algorithm for Well Assessment: A Case Study in Dili, Timor-Leste

This paper investigate to use of information technology, i.e. machine learning algorithms for water assessment in Timor-Leste. It is essential to access clean water to ensure the safety for humans and others livings in this world. The Water Quality Index (WQI) is the standard tool for assessing water quality, which can be calculated from physicochemical and microbiological parameters. However, in developing countries, it is continuing need to bring water and energy for the most disadvantaged, make it necessary to find new solutions. In such case, missing-value imputation and machine learning models are useful for classifying water samples into suitable or unsuitable with significant accuracy. Some imputation methods were tested, and four machine learning algorithms were explored: logistic regression, support vector machine, random forest, and Gaussian naïve Bayes. We obtained a dataset with 368 observations from 26 groundwater sampling points in Dili city of Timor-Leste. According to experimental results, it is found that 64% of the water samples are suitable for human consumption. We also found k-NN imputation and random forest method were the clear winners, achieving 96% accuracy with three-fold cross validation. The analysis revealed that some parameters significantly affected the classification results.