References

  1. US Environmental Protection Agency (EPA), EPA Enforcement: Preventing Backup of Municipal Sewage into Basements, Report No. 325-N-06-001, Office of Enforcement and Compliance Assurace, Washington, DC, 2006.
  2. US Environmental Protection Agency (EPA), Collection Systems O&M Fact Sheet: Sewer Cleaning and Inspection, Report No. 832-F-99-031, Office of Water, Washington, DC, 1999.
  3. K. Miller, K. Costa, D. Cooper, How to Upgrade and Maintain Our Nation’s Wastewater and Drinking-Water Infrastructure, Center for American Progress, Washington, DC, 2012. Available from: https://cdn.americanprogress.org/wp-content/uploads/2012/10/MillerWaterInfrastructureReport.pdf .
  4. J.P. Davies, B.A. Clarke, J.T. Whiter, R.J. Cunningham, Factors influencing the structural deterioration and collapse of rigid sewer pipes, Urban Water, 3 (2001) 73–89.
  5. G. Kley, N. Caradot, Review of sewer deterioration models, Project acronym: SEMA, Report D 1.2, Kompetenzzentrum Wasser Berlin gGmbH, Berlin, Germany, 2013. Available from: http://www.kompetenz-wasser.de/wp-content/uploads/2017/05/d12_sema_review_of_sewer_deterioration_models.pdf
  6. N. Caradot, M. Riechel, M. Fesneau, N. Hernandez, A. Torres, H. Sonnenberg, E. Eckert, N. Lengemann, J. Waschnewski, P. Rouault, Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany, J. Hydroinformatics (2018) doi:10.2166/ hydro.2018.217.
  7. R. Baur, R. Herz, Selective inspection planning with ageing forecast for sewer types, Water Sci. Technol., 46 (2002) 389– 396.
  8. F. Chughtai, T. Zayed, Infrastructure condition prediction models for sustainable sewer pipelines, J. Perform. Constr. Facil., 22 (2008) 333–341.
  9. E.V. Ana, W. Bauwens, Modeling the structural deterioration of urban drainage pipes: The state-of-the-art in statistical methods, Urban Water J., 7 (2010) 47–59.
  10. D.H. Tran, A.W.M. Ng, B.J.C. Perera, S. Burn, P. Davis, Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes, Urban Water J., 3 (2010) 175–184.
  11. D. Ballabio, V. Consonni, Classification tools in chemistry. Part 1: Linear models. PLS-DA, Anal. Meth., 5 (2013) 3790–3798.
  12. M. Alvarez-Guerra, D. Ballabio, J.M. Amigo, J.R. Viguri, R. Bro, A chemometric approach to the environmental problem of predicting toxicity in contaminated sediments, J. Chemometrics, 24 (2010) 379–386.
  13. M. Alvarez-Guerra, D. Ballabio, J.M. Amigo, R. Bro, J.R. Viguri, Development of models for predicting toxicity from sediment chemistry by partial least squares-discriminant analysis and counter-propagation artificial neural networks, Environ. Pollut., 158 (2010) 607–614.
  14. D. Ballabio, M. Vasighi, A MATLAB toolbox for self organizing maps and supervised neural network learning strategies, Chemomet. Intell. Lab. Syst., 118 (2012) 24–32.
  15. J. Zupan, M. Novic, I. Ruisánchez, Kohonen and counterpropagation artificial neural networks in analytical chemistry, Chemomet. Intell. Lab. Syst., 38 (1997) 1–23.
  16. W. Melssen, R. Wehrens, L. Buydens, Supervised Kohonen networks for classification problems, Chemomet. Intell. Lab. Syst., 83 (2006) 99–113.
  17. D. Ballabio, M. Vasighi, V. Consonni, M. Kompany-Zareh, Genetic algorithms for architecture optimisation of counter- propagation artificial neural networks, Chemomet. Intell. Lab. Syst., 105 (2011) 56–64.
  18. Modeling analysis report on XP-SWMM operated under the Chemomet. Intell. Lab. Syst. (BTL) scheme for sewer systems in Jinju City, Quarterly report for the financial year 2015–2017, Jinju City, Korea, 2018.
  19. S.J. Ki, J.-H. Kang, S.W. Lee, Y.S. Lee, K.H. Cho, K.-G. An, J.H. Kim, Advancing assessment and design of stormwater monitoring programs using a self-organizing map: characterization of trace metal concentration profiles in stormwater runoff, Water Res., 45 (2011) 4183–4197.
  20. T. Kohonen, Self-organizing Maps, 3rd ed., Springer Series in Information Sciences, Vol. 30, Springer-Verlag, Berlin, Heidelberg, New York, USA, 2001, p. 502.
  21. J. Vesanto, Data Exploration Process Based on the Self-organizing Map, Acta Polytechnica Scandinavica, Mathematics and Computing Series No. 115, Finnish Academies of Technology, Espoo, Finland, 2002.
  22. J. Vesanto, J. Himberg, E. Alhoniemi, J. Parhankangas, SOM Toolbox for Matlab 5, Report A57, SOM Toolbox Team, Helsinki University of Technology, Espoo, Finland, 2000.