18 citations as recorded by crossref.

1. Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review M. Rahim et al. 10.3390/w12010294
2. Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach M. Schaffer et al. 10.1016/j.energy.2024.130351
3. Effects of the COVID-19 Lockdown on Water Consumptions: Northern Italy Case Study S. Alvisi et al. 10.1061/(ASCE)WR.1943-5452.0001481
4. Water Consumption Pattern Analysis Using Biclustering: When, Why and How M. Silva et al. 10.3390/w14121954
5. A clustering solution for analyzing residential water consumption patterns M. Rahim et al. 10.1016/j.knosys.2021.107522
6. Functional data clustering via information maximization X. Li et al. 10.1080/00949655.2023.2215371
7. Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy R. Padulano & G. Del Giudice 10.1007/s11269-018-2140-0
8. A cluster analysis approach to sampling domestic properties for sensor deployment T. Menneer et al. 10.1016/j.buildenv.2023.110032
9. Effectiveness of smart meter-based urban water loss assessment in a real network with synchronous and incomplete readings C. Bragalli et al. 10.1016/j.envsoft.2018.10.010
10. Unsupervised Bayesian Nonparametric Approach with Incremental Similarity Tracking of Unlabeled Water Demand Time Series for Anomaly Detection . Chan & . Chin 10.3390/w11102066
11. Dynamic Time Warping Clustering to Discover Socioeconomic Characteristics in Smart Water Meter Data D. Steffelbauer et al. 10.1061/(ASCE)WR.1943-5452.0001360
12. On the importance of similarity characteristics of curve clustering and its applications A. Cheam & M. Fredette 10.1016/j.patrec.2020.04.024
13. Exploiting high-resolution data to investigate the characteristics of water consumption at the end-use level: A Dutch case study F. Mazzoni et al. 10.1016/j.wri.2022.100198
14. Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea K. Koo et al. 10.3390/su13116056
15. Interval-valued functional clustering based on the Wasserstein distance with application to stock data L. Sun et al. 10.1016/j.ins.2022.05.112
16. Contextualising household water consumption patterns in England: A socio-economic and socio-demographic narrative H. Abu-Bakar et al. 10.1016/j.clrc.2023.100104
17. Advanced household profiling using digital water meters M. Rahim et al. 10.1016/j.jenvman.2021.112377
18. Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning G. Bethke et al. 10.1039/D0EW00724B