Fatima Imtiaz

The critical issue of water deficiency is worsened by widespread wastage in both domestic and industrial sectors globally. A survey conducted by some international organizations indicates that only Pakistan is wasting ten trillion gallons of water. To address this challenge, our study introduces a comprehensive water usage optimization system by using the machine learning opportunities to help improve the classification and prediction of water quality (WQ). Also, using the sensor meters to collect real-time water quality (WQ) parameters in order to facilitate categorization of water sample. The real-time data collected from the sensor meters would be transferred in the storage for further analysis and usage. Several machine learning algorithms are used for the classification. The classifiers include Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and K-nearest neighbors (KNN). The dataset used for the categorization of water consists of 3277 samples. The water quality parameters used for the categorization were water pH, Turbidity, and Total dissolved solids (TDS) values. The stacked classifiers take the water parameters and provide an appropriate approach for forecasting water quality as accurately as possible. Beyond classification, our system provides targeted recommendations for recycling and reusing water based on its categorized quality. These recommendations are tailored to support sustainable water practices at both household and industrial levels. By combining real-time sensing with intelligent prediction and tailored guidance, the proposed system not only enhances water management but also contributes to long-term conservation efforts. This study serves as a step forward in the integration of smart technologies for sustainable resource utilization.