International Journal of Science, Engineering and TechnologyAuthors: K. Vigneshwar, A. Vedhika, B. Sai Teja, B. Josmitha
Abstract: Drinking Water Supply (DWS) systems are among the most essential and sensitive infrastructures required for maintaining urban life and public health across the world. In Europe, rapid population growth combined with aging and obsolete water supply infrastructure has created significant challenges in ensuring safe and continuous water distribution. Maintaining high water quality standards is critical not only for providing clean water for daily consumption but also for preventing health hazards caused by contamination. Traditional water quality monitoring methods mainly rely on periodic laboratory testing of parameters such as pH, turbidity, dissolved oxygen, and bacterial content. However, these testing procedures generally require 24–48 hours to produce results, creating a delay in identifying contamination and increasing the risk of bacterial spread within the water distribution network. To address these issues, this study proposes an Exploratory Data Analysis (EDA) based model for water quality assessment and prediction. The proposed model considers two major dimensions: water quality parameters and water quality score. Furthermore, machine learning techniques are applied to predict water quality changes within the DWS system. In this research, the Random Forest algorithm is implemented using PyCaret for efficient model development and analysis. A case study was conducted on an industrial water supply system to evaluate the model’s effectiveness. The preliminary results demonstrate that the proposed approach can successfully analyze and predict water quality conditions, helping authorities improve monitoring efficiency and reduce response time to contamination risks.
DOI: http://doi.org/10.5281/zenodo.20425611
