Determining Potability Based on Heavy Metal Ion Analysis of Water using Machine Learning

In this study, a comprehensive methodology for assessing water quality that integrates heavy metal pollution indices with advanced machine learning techniques was proposed, which includes Support Vector Machine (SVM), Decision Trees (DT), Random Forest, and XGBoostHcritical indicators of contamination of heavy metals. In this approach, models achieved exceptional accuracy rates. SVM demonstrates 88.57% accuracy, DT achieves 91.96% and Random Forest further enhances predictive capabilities with an accuracy of 94.11%. Notably, with an accuracy of 93.28%, XGBoost also makes a substantial contribution. This innovative approach enables real-time monitoring and proactive management of water resources, offering a robust tool for addressing environmental difficulties brought on by heavy metal pollution. By integrating machine learning algorithms, we provide insights into water quality dynamics, aiding in early detection and mitigation of contamination risks. Moreover, the inclusion of Random Forest enhances model robustness and adaptability across diverse environmental settings. This work underscores the importance of leveraging data-driven methodologies to safeguard environmental health and ensure sustainable water management practices. By combining indices with advanced machine learning techniques, we offer a scalable and effective solution for addressing water contamination challenges, thereby contributing to Improved efforts to protect the environment and better outcomes for public health.