Identifying Anomalies in Water Pump Systems Using Machine Learning and an Integrated Ensemble Method

Asustainable and continuous water supply requires water pump systems that operate effectively and reliably. Abnormalities in these systems, including equipment malfunctions or fluctuations in water flow, can have detrimental impacts on operations and pose safety risks. To maintain a sustainable water supply, water pump systems must be robust and dependable. This study investigates the use of an ensemble of machine learning techniques, specifically the Random Forest algorithm and K-Nearest Neighbor (KNN) algorithms, for anomaly detection in water pump systems to ensure sustainable and continuous water supply. Real-time sensor data from water pump installations was utilized for analysis. The ensemble model demonstrated over 99% accuracy in detecting abnormalities with low false positive rates across various datasets. The study showcases the reliability and practical applicability of this methodology in enhancing predictive maintenance, reducing downtime, and extending the lifespan of water pump systems. The findings highlight the effectiveness of integrating data-driven methodologies for real-time monitoring and maintenance automation.