23 citations as recorded by crossref.

1. Effects of the COVID-19 Lockdown on Water Consumptions: Northern Italy Case Study S. Alvisi et al.
2. Water Consumption Pattern Analysis Using Biclustering: When, Why and How M. Silva et al.
3. A clustering solution for analyzing residential water consumption patterns M. Rahim et al.
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5. Effectiveness of smart meter-based urban water loss assessment in a real network with synchronous and incomplete readings C. Bragalli et al.
6. Unsupervised Bayesian Nonparametric Approach with Incremental Similarity Tracking of Unlabeled Water Demand Time Series for Anomaly Detection T. Chan & C. Chin
7. Uncovering urban water consumption patterns through time series clustering and entropy analysis R. Wang et al.
8. On the importance of similarity characteristics of curve clustering and its applications A. Cheam & M. Fredette
9. Exploiting high-resolution data to investigate the characteristics of water consumption at the end-use level: A Dutch case study F. Mazzoni et al.
10. Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea K. Koo et al.
11. Advanced household profiling using digital water meters M. Rahim et al.
12. Enhancing the accuracy and reliability of non-online metered demand allocation in water distribution systems through dynamic spatiotemporal population distribution Q. Zhang et al.
13. Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review M. Rahim et al.
14. Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach M. Schaffer et al.
15. Functional data clustering via information maximization X. Li et al.
16. Generating zonal hourly water consumption patterns in water distribution networks using end-use data R. Mazaheri et al.
17. Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy R. Padulano & G. Del Giudice
18. A cluster analysis approach to sampling domestic properties for sensor deployment T. Menneer et al.
19. Dynamic Time Warping Clustering to Discover Socioeconomic Characteristics in Smart Water Meter Data D. Steffelbauer et al.
20. Temporal Segmentation of Urban Water Consumption Patterns Based on Non-Parametric Density Clustering A. Kapanski et al.
21. Interval-valued functional clustering based on the Wasserstein distance with application to stock data L. Sun et al.
22. Contextualising household water consumption patterns in England: A socio-economic and socio-demographic narrative H. Abu-Bakar et al.
23. Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning G. Bethke et al.