Unsupervised Water Leakage Detection Using IoT Devices and Anomaly Detection Algorithms - Cabral, Robert A.; Gonçalves, Robert V. O.; Menezes, Maria V.; Aguilar, Paulo A. C.; Stancanelli, Elvis M. G.; Callado, Arthur C.; Gonçalves, Enyo J. T. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247574, 2025)
This work proposes a water leakage detection system for residential settings using low-cost sensors and unsupervised machine learning algorithms. Water consumption data is collected by an STM32 microcontroller connected to a pulse-output water meter and transmitted via LoRaWAN for remote analysis. The models used Isolation Forest and Elliptic Envelope require no labeled data. Evaluation was conducted using three datasets, including one collected by the authors. The models achieved an average F1-score of 0.8579, performing well even in high-variability scenarios. The proposed solution stands out for its scalability, low cost, and adaptability to different household consumption patterns.