An intelligent pattern recognition model to automate the categorisation of residential water end-use events

Environmental Modelling and Software

Volumn 47, Issue , 2013, Pages 108-127

  Nguyen, K A ^a   Stewart, R A ^a   Zhang, H ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

Dynamic time warping algorithm; Gradient vector filtering; Hidden Markov model; Residential water flow trace disaggregation; Water demand management; Water end use event; Water micro component

Indexed keywords

DISAGGREGATION; DYNAMIC TIME WARPING ALGORITHMS; END-USES; GRADIENT VECTORS; WATER DEMAND MANAGEMENT; WATER MICRO COMPONENTS;

ALGORITHMS; BLIND SOURCE SEPARATION; DATA TRANSFER; FLOW OF WATER; HIDDEN MARKOV MODELS; HYDRAULICS; PATTERN RECOGNITION; WATER SUPPLY;

HOUSING;

AUTOMATION; DATABASE; MARKOV CHAIN; PATTERN RECOGNITION; WATER DEMAND; WATER FLOW; WATER MANAGEMENT;

AUSTRALIA; QUEENSLAND;

EID: 84879297869     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2013.05.002     Document Type: Article

References (55)

1. Baum L.E., Petrie T., Soules G., Weiss N. Amaximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics 1970, 41:164. 10.1214/aoms/1177697196.
2. Beal C., Stewart R.A. South East Queensland Residential end Use Study: Final Report January 2012, Technical Report No. 47 for Urban Water Security Research Alliance, Griffith University and Smart Water Research Centre.
3. Beal C., Stewart R.A., Fielding K. Anovel mixed method smart metering approach to reconciling differences between perceived and actual residential end use water consumption. Journal of Cleaner Production 2011, 10.1016/j.jclepro.2011.09.007.
4. Beal C., Stewart R.A., Huang T.T., Rey E. SEQ residential end-use study. Journal of the Australian Water Association 2011, 38(1):80-84.
5. Bhadeshia H.K.D.H. Neural networks in materials science. ISIJ International 1999, 39(10):966-979. 10.2355/isijinternational.39.966.
6. Bishop C.M. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford, ISBN: 0-19-853864-2.
7. Blokker E., Vreeburg J., Van Dijk J. Simulating residential water demand with a stochastic end use model. Journal of Water Resources Planning and Management 2010, 136:19-26.
8. Boashash B. Time Frequency Signal Analysis and Processing. A Comprehensive Reference 2003, Elsevier.
9. Cha S.H., Tappert C.C. Agenetic algorithm for constructing compact binary decision trees. Journal of Pattern Recognition Research 2009, 4(1):1-13. http://www.jprr.org/index.php/jprr/article/view/44/25.
10. CSIRO SEQ Residential Water End Use Study: Validation Trial of CSIRO End Use Sensor 2012, Technical Report No. 91, Urban Water Security Research Alliance.
11. Da Y., Xiurun G. An improved PSO-based ANN with simulated annealing technique. New Aspects in Neurocomputing: 11th European Symposium on Artificial Neural Networks 2005, Elsevier. 10.1016/j.neucom. T. Villmann (Ed.).
12. David C.H., Yang Z.L., Hong S. Regional-scale river flow modelling using off-the-shelf runoff products, thousands of mapped rivers and hundreds of stream flow gauges. Environmental Modelling & Software 2013, 42:116-132.
13. Deng H., Runger G., Tuv E. Bias of importance measures for multi-valued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN) 2011, http://enpub.fulton.asu.edu/hdeng3/MultiICANN2011.pdf.
14. Dorini G., Jonkergouw G., Kapelan Z., Pierro F., Khu S.T., Savic D. An efficient algorithm for sensor placement in water distribution systems. Proceedings of 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, Ohio, USA, August 27-30, 2006 2006.
15. Dürrenmatt D.J., Gujer W. Data-driven modelling approaches to support wastewater treatment plant operation. Environmental Modelling & Software 2012, 30:47-56.
16. Eggers G., Politti E., Woo H., Cho K.-H., Cho H., Benjankar R., Lee N.-J., Lee H. Dynamic vegetation model as a tool for ecological impact assessments of dam operation. Journal of Hydro-environment Research, Special Issue on Ecohydraulics: Recent Research and Applications 2012, 6(2):151-161.
17. Ephraim Y., Merhav N. Hidden Markov processes. IEEE Transactions on Information Theory 2002, 48:1518-1569.
18. Froehlich J., Larson E., Saba E., Campell T., Atlas L., Fogarty J., Patel S. ALongitudinal Study of Pressure Sensing to Infer Real-world Water Usage Events in the Home 2011, Computer Science and Engineering. University of Washington.
19. Froehlich J.E., Larson E., et al. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. Proc.. of UbiComp 2009, Orlando, Florida, USA 2009, 235-244.
20. Ghahramani Z., Jordan M.I. Factorial hidden Markov models. Machine Learning 1997, 29(2/3):245-273. 10.1023/A:1007425814087.
21. Glasgow H.B., Burkholder J.M., Reed R.E., Lewitus A.J., Kleinman J.E. Real-time remote monitoring of water quality: a review of current applications, and advancements in sensor, telemetry, and computing technologies. Journal of Experimental Marine Biology and Ecology 2004, 300:409-448.
22. Haykin S. Neural Networks: A Comprehensive Foundation 1999, Prentice Hall.
23. Heinrich M. Water End-use and Efficiency Project (WEEP) 2006, Final Report, BRANZ Study Report 159, Branz, Judgeford, New Zealand.
24. Hertz J., Palmer R.G., Krogh A.S. Introduction to the Theory of Neural Computation 1990, Perseus Books.
25. Howell D.C. Fundamental Statistics for the Behavioural Sciences 2004, Thompson Brookes/Cole, Belmont, CA.
26. Jacobs H.E. The first reported correlation between end-use estimates of residential water demand and measured use in South Africa. Water SA 2007, 33(4):549-558.
27. Kowalski M., Marshallsay D. Using measured micro-component data to model the impact of water conservation strategies on the diurnal consumption profile. Water Science and Technology: Water Supply 2005, 5(3-4):145-150.
28. Lindim C., Hayter E.J., Pinho J.L.S., Vieira J.M.P. Modeling thermal structure variations in a stratified reservoir. Proceeding of the Fifth Biennial Conference of the International Environmental Modelling and Software Society, Ottawa, Canada, 5-8 July 2010 2010.
29. Loh M., Coghlan P. Domestic Water Use Study in Perth, Western Australia 1998 to 2000 2003, Water Corporation of Western Australia.
30. Mayer P., DeOreo W., Towler E., Martien L., Lewis D. Tampa Water Department Residential Water Conservation Study: The Impacts of High Efficiency Plumbing Fixture Retrofits in Single-family Homes 2004, Aquacraft Inc. Water Engineering and Management, USA.
31. Mead N. Investigation of Domestic End Use 2008, Honours thesis, Faculty of Engineering & Surveying, University of Southern Queensland, Toowoomba, Australia.
32. Murlà D., Monistrol A., Poch M., Sunyer D. Development of a modular Environmental Decision Support System for the Integrated Management of the Urban Wastewater Cycle at River Basin Scale. Proceeding of the Fifth Biennial Conference of the International Environmental Modelling and Software Society, Ottawa, Canada, 5-8 July 2010 2010.
33. Muller M. Information Retrieval for Music and Motion 2007, Chapter 4, Springer.
34. Myers C.S., Rabiner L.R. Acomparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal 1981, 60:1389-1409.
35. Nguyen K.A., Zhang H., Stewart R.A. Development of an intelligent model to categorise residential water end use events. Journal of Hydro-Environment Research 2013, 10.1016/j.jher.2013.02.004.
36. Quinn N.W.T., Ortega R., Rahilly P.J.A., Royer C.W. Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal wetland real-time water quality management. Environmental Modelling & Software 2010, 25:1045-1058.
37. Rabiner L., Juang B. Fundamentals of Speech Recognition 1993, Prentice-Hall, Inc., Chapter 4.
38. Rabiner L.R. ATutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Readings in Speech Recognition 1990, Morgan Kaufmann Publishers Inc.
39. Roberts P. Yarra Valley Water 2004 Residential End-use Measurement Study 2005, Yarra Valley Water, Melbourne.
40. Satish L., Gururaj B.I. Use of hidden Markov models for partial discharge pattern classification. IEEE Transactions on Dielectrics and Electrical Insulation 2003.
41. Sakoe H., Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 1978, 26(1):43-49. (Online). Available from:. http://ieeexplore.ieee.org/xpls/absall.jsp%3farnumber%3d1163055.
42. Schimak G., Rizzoli A., Watson K. Sensors and the Environment - Modelling & ICT Challenges 2010, Environmental Modelling & Software.
43. Shapiro L., George S. Computer Vision 1998, 157-158. Prentice-Hall, Inc., Upper Saddle River, New Jersey, 215-216, 299-300.
44. Starner T., Pentland A. Real-Time American Sign Language Visual Recognition From Video Using Hidden Markov Models 1995, Master's thesis, MIT, Program in Media Arts.
45. Stewart R.A., Willis R., Giurco D., Panuwatwanich K., Capati G. Web-based knowledge management system: linking smart metering to the future of urban water planning. Australian Planner 2010, 47:66-74.
46. Storey M.V., Gaag B.V.D., Burns B.P. Advances in on-line drinking water quality monitoring and early warning systems. Water Research 2011, 45(2011):741-747.
47. Tapia E., Intille S.S., Larson K. Activity recognition in the home using simple and ubiquitous sensors. LNCS 2004, vol. 3001:158-175. Springer, Heidelberg. A. Ferscha, F. Mattern (Eds.).
48. Usländer T., Jacques P., Simonis I., Watson K. 2010. Designing environmental software applications based upon an open sensor service architecture. Environmental Modelling & Software 2010, 25:977-987.
49. White D.H., Beynon N., Kingma O. Identifying opportunities for achieving water savings throughout the Murray-Darling Basin. Environmental Modelling & Software 2006, 21:1013-1024.
50. Willis R.M., Stewart R.A., Panuwatwanich K., Jones S., Kyriakides A. Alarming visual display monitors affecting shower end-use water and energy conservation in Australian residential households. Resources, Conservation and Recycling 2010, 54(12):1117-1127.
51. Willis R.M., Stewart R.A., Williams P., Hacker C., Emmonds S., Capati G. Residential potable and recycled water end-uses in a dual reticulated supply system. Desalination 2011, 272(1-3):201-211.
52. Willis R.M., Stewart R.A., Giurco D.P., Talebpour M., Mousavinejad A. End use water consumption in households: impact of socio-demographic factors and efficient devices. Journal of Cleaner Production 2011, 2011:1-9.
53. Yiee H.H., Baskin L.S. Use of internal stent, external transanastomotic stent or no stent during pediatric pyeloplasty: a decision tree cost-effectiveness analysis. The Journal of Hydrology 2011, 185(2):673-681.
54. Yuan Y., Shaw M.J. Induction of fuzzy decision trees. Fuzzy Sets and Systems 1995, 69:125-139.
55. Zhang H., Jeng D.-S., Cha D., Blumenstein M. Parametric study on the prediction of wave-induced liquefaction using an artificial neural network model, SI 50. Journal of Coastal Research 2007, 374-378.