| Title |
Anomalous water use detection using machine learning / |
| Authors |
Kulikovas, Lukas ; Packevičius, Šarūnas |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2023: Information society and university studies 2023: proceedings of the 28th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 12, 2023 / edited by: A. Lopata, T. Krilavičius, I. Veitaitė, A. García-Holgado.. Aachen : CEUR-WS. 2023, vol. 3575, p. 22-33.. ISSN 1613-0073 |
| Keywords [eng] |
anomalous water use detection ; unsupervised learning ; semi-supervised learning |
| Abstract [eng] |
Water is an essential resource that is necessary for human life, agriculture, and industry. Numerous countries confront water shortages and inefficient water usage. Anomalous water usage detection is an important task in the efficient management of water resources and the prevention of water leaks. In this publication, we present a comparison between various machine learning models to detect unusual patterns in water usage data. All the machine learning models were tested on a real-world water usage dataset. The performance of each model was evaluated by accuracy, precision, recall, F1-score, ROC AUC, and MAE scores. The results indicate that PCA outlier detector can accurately detect uncommon patterns in water usage data. Our results outlined in this paper might be utilized by either individual homeowners or water utility corporations to detect water leaks more quickly and hence minimize water wastage. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
