References

  1. T. Shiao, T. Luo, D. Maggo, E. Loizeaux, C. Carson, S. Nischal, The India Water Tool, 2016, The World Resources Institute and Columbia Water Centre, Washington DC, Available online: http://www.indiawatertool.in.
  2. M. Ramachandran, Guidance notes for continuous water supply (24–7 supply) – A guide to project preparation implementation and appraisal: A report by water and sanitation program, 2014, http://sanitation.indiawaterportal.org/english/node/2351.
  3. A. Campisano, G. D’Amico, C. Modica, Water saving and cost analysis of large-scale implementation of domestic rain water harvesting in minor Mediterranean Islands, Water, 9 (2017) 916.
  4. H.E. Mutikanga, S.K. Sharma, K. Vairavamoorthy, Methods and tools for managing losses in water distribution systems, J. Water Resour. Plann. Manage., 139 (2012) 166174.
  5. E.A. Lee, The past, present and future of cyber-physical systems: a focus on models, Sensors, 15 (2015) 4837–4869.
  6. E.A. Lee, S.A. Seshia, Introduction to Embedded Systems: A Cyber-Physical Systems Approach, Mit Press, 2016.
  7. J. Lin, A. Miller, S. Sedigh, Integrated Cyber-physical Simulation of Intelligent Water Distribution Networks, INTECH Open Access Publisher, 2011.
  8. J. Lin, S. Sedigh, A. Miller, A game-theoretic approach to decision support for intelligent water distribution, In: 2011 44th Hawaii International Conference on System Sciences, Kauai, HI, USA, 2011, ISSN 1530–1605.
  9. M. Suresh, U. Manohary, A.G. Ry, R. Stoleru, M.K.M. Sy, A cyber-physical system for continuous monitoring of water distribution systems, In: 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Larnaca, Cyprus. 2014, ISSN 2160–48863.
  10. Z. Wang, H. Song, D.W. Watkins, K.G. Ong, P. Xue, Q. Yang, X. Shi, Cyber physical systems for water sustainability: challenges and opportunities, IEEE Commun. Mag., 53 (2015) 216222.
  11. C.-Y. Lin, S. Zeadally, T.-S. Chen, C.-Y. Chang, Enabling cyber physical systems with wireless sensor networking technologies, Int. J. Distrib. Sens. Netw., 8 (2012a) 489794.
  12. J. Lin, A. Hurson, S. Sedigh, Knowledge Management for Fault- Tolerant Water Distribution, in: Large Scale Network-Centric Distributed Systems, 2012, pp. 649–677.
  13. J. Lin, S. Sedigh, A.R. Hurson, Ontologies and Decision Support for Failure Mitigation in Intelligent Water Distribution Networks, In: 2012 45th Hawaii International Conference on System Sciences, Maui, HI, USA, 2012b, ISSN 1530–1605.
  14. E.K. Wang, Y. Ye, X. Xu, S.-M. Yiu, L.C.K. Hui, K.-P. Chow, Security issues and challenges for cyber physical system, In 2010 IEEE/ACM Int’l Conference on Green Computing and Communications Int’l Conference on Cyber, Physical and Social Computing, Hangzhou, China, 2010, ISBN Print ISBN: 978-1- 4244-9779-9; CD-ROM ISBN: 978-0-7695-4331-4.
  15. K. Patil, A. Ghosh, D. Das, S.K. Vuppala, Iwcmse: Integrated water consumption monitoring solution for enterprises, Proc. 2014 International Conference on Interdisciplinary Advances in Applied Computing, ACM, 2014.
  16. Q. Zhang, A. Rahman, C. D’este, Impute vs. ignore: missing values for prediction, In: Neural Networks (IJCNN), The 2013 International Joint Conference on, IEEE, 2013.
  17. H.T. Wubetie, Missing data management and statistical measurement of socio-economic status: application of big data, J. Big Data, 4 (2017) 47.
  18. I.M. Pires, N.M. Garcia, N. Pombo, F. Florez-Revuelta, From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices, Sensors, 16 (2016) 184.
  19. K. Lakshminarayan, S.A. Harp, T. Samad, Imputation of missing data in industrial databases, Appl. Intell., 11 (1999) 259–275.
  20. A. Kowarik, M. Templ, Imputation with r package vim, J. Stat. Software, 74 (2016) 1–16.
  21. M. Templ, A. Alfons, A. Kowarik, B. Prantner, Vim: Visualization and imputation of missing values, 2011, URL http://CRAN.R-project. org/package= VIM. R package version, 3(0).
  22. B. Prantner, Visualization of imputed values using the R-package VIM, 2011.
  23. J. Honaker, G. King, M. Blackwell, M.M. Blackwell, Package amelia, 2010.
  24. T. Hastie, R. Mazumder, Softimpute: Matrix Completion via Iterative Soft-Thresholded SVD, R package, Version 1, 2015.
  25. U. Pillai, V. Murthy, I. Selesnick, Missing data recovery using low rank matrix completion methods, In: Radar Conference (RADAR), IEEE, 2012.
  26. Y.-L. Zheng, L.-P. Zhang, X.-L. Zhang, K. Wang, Y.-J. Zheng, Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China, PLoS One, 10 (2015) e0116832.
  27. M. Herrera, L. Torgo, J. Izquierdo, R. Pérez-García, Predictive models for forecasting hourly urban water demand, J. Hydrol., 387 (2010) 141–150.
  28. G.-Z. Wu, K. Sakaue, S. Murakawa, Verification of calculation method using Monte Carlo Method for water supply demands of office building, Water, 9 (2017) 376.
  29. L. Torgo, M.L. Torgo, Package dmwr, Comprehensive R Archive Network, 2013.
  30. J. Nookhong, N. Kaewrattanapat, Efficiency comparison of data mining techniques for missing-value imputation, J. Ind. Intell. Inf., 3 (2015) 305–309.
  31. M.B. Abhishek, N.S.V. Shet, Data transmission unit and web server interaction to monitor water distribution: a cyberphysical system perspective, Int. J. Adv. Sci. Eng. Inf. Technol., 8 (2018) 1307–1312.