Real-time clustering for priority evaluation in a water distribution system

Nowadays with the development of smart infrastructure for water resource management, there is an increased need for efficient operation and management of water distribution infrastructures. In this paper, we propose a system for real-time clustering system priority evaluation in a water distribution system. Data clustering algorithms are modified for real-time scenarios providing decision support for management of water distribution system. The proposed method is compared to the standard clustering methods in terms of accuracy and real-time capabilities.

[1]  Prashant Malik,et al.  Cassandra: structured storage system on a P2P network , 2009, PODC '09.

[2]  Bin Wu,et al.  Social network users clustering based on multivariate time series of emotional behavior , 2014 .

[3]  Shubha Singh,et al.  A Survey of Clustering Techniques , 2010 .

[4]  Ciprian Lupu,et al.  Modeling the Effects of Leaks on Measured Parameters in a Water Distribution System , 2017, 2017 21st International Conference on Control Systems and Computer Science (CSCS).

[5]  Hwee Pink Tan,et al.  Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications , 2014, IEEE Communications Surveys & Tutorials.

[6]  W. R. Furnass,et al.  Clustering and classification of aggregated smart meter data to better understand how demand patterns relate to customer type , 2016 .

[7]  Mahesh Kumar,et al.  Clustering seasonality patterns in the presence of errors , 2002, KDD.

[8]  Axel Wismüller,et al.  Cluster Analysis of Biomedical Image Time-Series , 2002, International Journal of Computer Vision.

[9]  Joseba Jokin Quevedo Casín,et al.  Water demand estimation and outlier detection from smart meter data using classification and Big Data methods , 2015 .

[10]  Paul R. Cohen,et al.  Multivariate Clustering by Dynamics , 2000, AAAI/IAAI.

[11]  Muhammad Umar,et al.  Integrative Review of Decentralized and Local Water Management Concepts as Part of Smart Cities (LoWaSmart) , 2016 .

[12]  Christoph Meinel,et al.  Real-time clustering of massive geodata for online maps to improve visual analysis , 2015, 2015 11th International Conference on Innovations in Information Technology (IIT).

[13]  Alioune Ngom,et al.  Multiple gene expression profile alignment for microarray time-series data clustering , 2010, Bioinform..

[14]  Wolfgang Kastner,et al.  Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns , 2013 .

[15]  Pavlos Protopapas,et al.  Finding anomalous periodic time series , 2009, Machine Learning.

[16]  Vipin Kumar,et al.  Discovery of climate indices using clustering , 2003, KDD '03.

[17]  Ana L. N. Fred,et al.  Data clustering using evidence accumulation , 2002, Object recognition supported by user interaction for service robots.

[18]  N. C. Turner,et al.  Hardware and Software Techniques for Pipeline Integrity and Leak Detection Monitoring , 1991 .