Ensemble of naïve Bayesian approaches for the study of biofilm development in drinking water distribution systems

References

• D. de Beer, R. Srinivansa, P. Stewart, Direct measurement of chlorine penetration into biofilms during disinfection, Appl. Environ. Microbiol. 60 (1994), pp. 4339–4344
   PubMed Web of Science ®Google Scholar
• L. Breiman, Bagging predictors, Mach. Learn. 24(2) (1996), pp. 123–140
   Web of Science ®Google Scholar
• S. Chowdhury, Heterotrophic bacteria in drinking water distribution system: A review, Environ. Monitor. Asses. 184(10) (2012), pp. 6087–6137 doi: 10.1007/s10661-011-2407-x
   PubMed Web of Science ®Google Scholar
• R. Christensen, Age effects on iron-based pipes in water distribution systems, Utah State University (2009), Master's thesis
   Google Scholar
• T. Cloete, D. Westard, S. van Vuuren, Dynamic response of biofilm to pipe surface and fluid velocity, Water Sci. Technol. 45 (2003), pp. 57–59
   Google Scholar
• N. Friedman, D. Geiger, M. Goldszmidt, Bayesian network classifiers, Mach. Learn. 29 (1997), pp. 131–163 doi: 10.1023/A:1007465528199
   Web of Science ®Google Scholar
• M. F. Gelves, Deterioro de la calidad del agua por el posible desprendimiento de las biopelículas en las redes de distribución de agua potable, Universidad de los AndesBogotá, Colombia (2005)
   Google Scholar
• J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann PublishersSan Francisco, CA (2006)
   Google Scholar
• K. Hornik, C. Buchta, A. Zeileis, Open-source machine learning: R meets Weka, Comput. Statist. 24 (2009), pp. 225–232 doi: 10.1007/s00180-008-0119-7
   Web of Science ®Google Scholar
• IWA, Effects of water age on distribution system water quality, Tech. rep. (2002)
   Google Scholar
• E. J. Keogh, M. J. Pazzani, Learning the structure of augmented Bayesian classifiers, Artif. Intell. Tools 11(4) (2002), pp. 587–601 doi: 10.1142/S0218213002001052
   Google Scholar
• R. Kohavi, Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid, in, 2nd International Conference on Knowkedge Discovery and Data Mining, E. Simoudis, J. Han, U. Fayyad, eds., lAAAI Press, Menlo Park, CA, (2002), pp. 202–207
   Google Scholar
• S. Kotsiantis, Combining bagging, boosting, rotation forest and random subspace methods, Artif. Intell. Rev. 35(3) (2011), pp. 223–240 doi: 10.1007/s10462-010-9192-8
   Web of Science ®Google Scholar
• J. Landis, G. Koch, The measurement of observed agreement for categorical data, Biometrics 33 (1977), pp. 159–174 doi: 10.2307/2529310
   PubMed Web of Science ®Google Scholar
• M. Lehtola, M. Laxandera, I. Miettinena, A. Hirvonec, T. Vartiainenb, P. Martikainenc, The effects of changing water flow velocity on the formation of biofilms and water quality in pilot distribution system, Water Res. 40 (2006), pp. 2151–2160 doi: 10.1016/j.watres.2006.04.010
   PubMed Web of Science ®Google Scholar
• C. Mains, Biofilm control in distribution systems,, Teach Brief 8(2) (2008), pp. 1–4
   Google Scholar
• D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, F. Leisch, e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, (2012), Available at http://CRAN.R-project.org/package=e1071, r package version 1.6-1.
   Google Scholar
• M. Momba, R. Kfir, S. Venter, T. Cloete, An overview of biofilm formation in distribution systems and its impact on the deterioration of water quality, Water SA 26(1) (2000), pp. 59–66
   Web of Science ®Google Scholar
• P. M. Niquette, P. Servais, R. Savoir, Impacts of pipe materials on densities of fixed bacterial biomass in a drinking water distribution system, Water Resour. 34 (2000), pp. 1952–1956
   Google Scholar
• R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2011), Available at http://www.R-project.org/, ISBN 3-900051-07-0.
   Google Scholar
• E. Ramos-Martínez, Evaluación del desarrollo de biofilms en los sistemas de distribución de agua potable mediante la extracción de conocimiento a través de los datos (knowledge discovery data – kdd), Universitat Politècnica de València (2012), Master's thesis
   Google Scholar
• E. Ramos-Martínez, M. Herrera, J. Izquierdo, R. Pérez-García, Pre-processing metadata on biofilm development in drinking water distribution systems, Hydroinformatics (2013), under review.
   Google Scholar
• B. Ripley, Pattern recognition and neuronal networks, Cambridge University PressCambridge, UK (1996)
   Google Scholar
• C. Sammut, G. I. Webb, Encyclopedia of Machine Learning, SpringerNew York (2011)
   Google Scholar
• J. Silhan, C. Corfitzen, H. J. Albrechtsen, Effect of temperature and pipe material on biofilm formation and survival of escherichia coli in used drinking water pipes: A laboratory-based study, Water Sci. Technol. 54 (2006), pp. 49–56 doi: 10.2166/wst.2006.447
   PubMed Web of Science ®Google Scholar
• M. Simoes, M. Pereira, M. Vieira, The role of hydrodynamic stress on the phenotypic characteristics of single and binary biofilms of pseudomonas fluorescens, Water Sci. Technol. 55 (2007), pp. 437–445 doi: 10.2166/wst.2007.288
   PubMed Web of Science ®Google Scholar
• M. Simoes, L. Simoes, I. Machado, M. Pereira, M. Vieira, Control of flow-generated biofilms with surfactants evidence of resistance and recovery, Food Bioprod. Process. 84 (2006), pp. 338–345 doi: 10.1205/fbp06022
   Web of Science ®Google Scholar
• Y. Tsai, Impact of flow velocity on the dynamic behaviour of biofilm bacteria, Biofouling 21 (2005), pp. 267–277 doi: 10.1080/08927010500398633
   PubMed Web of Science ®Google Scholar
• Z. Tsvetanova, Study of biofilm formation on different pipe materials in a model of drinking water distribution system and its impact on microbiological water quality, Chemicals as Intentional and Accidental Global Environmental ThreatsL. Simeonov, E. Chirila, DordrechtThe Netherlands (2006), pp. 463–468 NATO Security through Science Series
   Google Scholar
• H. A. Videla, L. K. Herrera, Microbiologically influenced corrosion: Looking to the future, international microbiology, Water Res. 8 (2005), pp. 169–180
   Google Scholar
• J. Wingender, F. Hans-Curt, Biofilms in drinking water and their role as reservoir for pathogens, Hyg. Environ. Health 214 (2011), pp. 417–423 doi: 10.1016/j.ijheh.2011.05.009
   PubMed Web of Science ®Google Scholar
• I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan KaufmannSan Francisco, CA (2005)
   Google Scholar
• J. Yu, D. Kin, T. Lee, Microbial diversity in biofilms on water distribution pipes of different materials, Water Sci. Technol. 61 (2010), pp. 163–171 doi: 10.2166/wst.2010.813
   PubMed Web of Science ®Google Scholar
• L. Zhou, Y. Zhang, G. Li, Effect of pipe material and low level disinfectants on biofilm development in a simulated drinking water distribution system, Zhejiang Univ. 10 (2009), pp. 725–731 doi: 10.1631/jzus.A0820486
   Web of Science ®Google Scholar