Publication Date: 2024/01/12
Abstract: The main work of our research aims to find out the Water Quality Index of bore water in our surrounding educational institutions using two machine learning algorithms. Our research work differentiates from other work by choosing Decision Tree, K-Nearest Neighbor algorithms, and their accuracy. We collected water samples from various resources and calculated the six important factors: salinity, total suspended solids (TDS), dissolved oxygen (DO), acidity and alkalinity (pH), and biochemical oxygen demand (BOD). Using efficient chemical methods, the quality parameters of water were examined. We created our dataset by utilizing these metrics, and the dataset is given as our chosen algorithm’s training and testing data. Finally, we got the WQI value with two different accuracies.
Keywords: Water quality Index, Decision Tree, KNN, Gini Index.
DOI: https://doi.org/10.5281/zenodo.10496097
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN276.pdf
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