Advancing Water Quality Monitoring in Smart Cities using Machine learning Techniques

In smart cities and urban environment, monitoring of water quality is very essential for environmental sustainability, resource optimization, cost management and streamlining the treatment process. In this research, an extensive dataset containing various contaminants such as aluminum, ammonia, arsenic, and others is utilized to effectively classify the safety level of water. Different machine learning and ensemble learning classifiers including LightGBM, XGBoost, CatBoost, Bagging, Gradient Boosting, Random Forest, Decision Tree, AdaBoost, MLP, and Extra Trees were implemented to conduct an empirical experimentation. Various performance metrics like Accuracy, Precision, Recall, F-1 Score, F-2 Score, Sensitivity, Specificity and AUC-ROC were used to evaluate the machine learning techniques utilized in this research. Experimental results indicate that the LightGBM ensemble technique outperformed other models with the accuracy of 97.13% and AUC-ROC of 98.99%, due to its optimized gradient boosting and effective processing of categorical features. This research contributes to the algorithmic approach for developing decision support tools for improving sustainable smart city water management techniques.