12. EPA (2010) Water quality event detection systems for drinking water contamination warning
systems: development, testing and application of CANARY. EPA/600/R-10/036. US Environmental Protection Agency, Washington, DC. http://www.epa.gov/ord
13. Murray R, Haxton T, McKenna SA, Hart DB, Klise K, Koch M, Vugrin ED, Martin S,
Wilson M, Cruz V, Cutler L (2010) Water quality event detection systems for drinking water
contamination warning systems development, testing, and application of CANARY. EPA/600/
R-10/036, Cincinnati, OH
14. Brill E (2014) Implementing machine learning algorithms for water quality event detection:
theory and practice. In: Clark RM, Hakim S (eds) Securing water and wastewater systems
series: protecting critical infrastructure, vol 2. Springer, Cham, pp 107–122
15. Skadsen J (2008) Distribution system on-line monitoring for detecting contamination and water
quality changes. J AWWA 100:7
16. Story MV, Van der Gaag B, Burns B (2011) Advances in on-line drinking water quality
monitoring and early warning systems. Water Res 45:741–747
17. Jeffrey Yang Y, Haught RC, Goodrich JA (2009) Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and
experimental results. J Environ Manage 90(8):2494–2506
18. Chang N-B, Pongsanone NP, Ernest A (2012) A rule-based decision support system for sensor
deployment in small drinking water networks. J Clean Prod 29–30:28–37
19. Helbling DE, VanBriesen JM (2008) Continuous monitoring of residual chlorine concentrations
in response to controlled microbial intrusions in a laboratory-scale distribution system. Water
Res 42(12):3162–3172
20. Mellisa P, Alexandera JH, Sakka N (2015) Mammograms classification using gray-level
co-occurrence matrix and radial basis function neural network. International conference on
computer science and computational intelligence (ICCSCI 2015). Procedia Comput Sci
59:83–91
21. Padmapriya P, Manikandan K, Jeyanthi K, Renuga V, Sivaraman J (2016) Detection and
classification of brain tumor using radial basis function. Indian J Sci Technol 9(1). https://doi.
org/10.17485/ijst/2016/v9i1/85758
22. Rajab T, Salleh R (2017) Classıfıcatıon of diabetes disease using backpropagation and radial
basis function network. UTM computing proceedings innovations in computing technology and
applications, vol 2
23. Mansourkhaki A, Berangi M, Haghiri M (2018) Comparative application of radial basis
function and multilayer perceptron neural networks to predict traffic noise pollution in Tehran
roads. J Ecol Eng 19(1):113–121
24. Chun-Cheng L, Weichih H (2011) A radial basis function neural network for the detection of
abnormal intra-QRS potentials. Computing in cardiology conference 2011, Hangzhou, China
166
E. Brill
systems: development, testing and application of CANARY. EPA/600/R-10/036. US Environmental Protection Agency, Washington, DC. http://www.epa.gov/ord
13. Murray R, Haxton T, McKenna SA, Hart DB, Klise K, Koch M, Vugrin ED, Martin S,
Wilson M, Cruz V, Cutler L (2010) Water quality event detection systems for drinking water
contamination warning systems development, testing, and application of CANARY. EPA/600/
R-10/036, Cincinnati, OH
14. Brill E (2014) Implementing machine learning algorithms for water quality event detection:
theory and practice. In: Clark RM, Hakim S (eds) Securing water and wastewater systems
series: protecting critical infrastructure, vol 2. Springer, Cham, pp 107–122
15. Skadsen J (2008) Distribution system on-line monitoring for detecting contamination and water
quality changes. J AWWA 100:7
16. Story MV, Van der Gaag B, Burns B (2011) Advances in on-line drinking water quality
monitoring and early warning systems. Water Res 45:741–747
17. Jeffrey Yang Y, Haught RC, Goodrich JA (2009) Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and
experimental results. J Environ Manage 90(8):2494–2506
18. Chang N-B, Pongsanone NP, Ernest A (2012) A rule-based decision support system for sensor
deployment in small drinking water networks. J Clean Prod 29–30:28–37
19. Helbling DE, VanBriesen JM (2008) Continuous monitoring of residual chlorine concentrations
in response to controlled microbial intrusions in a laboratory-scale distribution system. Water
Res 42(12):3162–3172
20. Mellisa P, Alexandera JH, Sakka N (2015) Mammograms classification using gray-level
co-occurrence matrix and radial basis function neural network. International conference on
computer science and computational intelligence (ICCSCI 2015). Procedia Comput Sci
59:83–91
21. Padmapriya P, Manikandan K, Jeyanthi K, Renuga V, Sivaraman J (2016) Detection and
classification of brain tumor using radial basis function. Indian J Sci Technol 9(1). https://doi.
org/10.17485/ijst/2016/v9i1/85758
22. Rajab T, Salleh R (2017) Classıfıcatıon of diabetes disease using backpropagation and radial
basis function network. UTM computing proceedings innovations in computing technology and
applications, vol 2
23. Mansourkhaki A, Berangi M, Haghiri M (2018) Comparative application of radial basis
function and multilayer perceptron neural networks to predict traffic noise pollution in Tehran
roads. J Ecol Eng 19(1):113–121
24. Chun-Cheng L, Weichih H (2011) A radial basis function neural network for the detection of
abnormal intra-QRS potentials. Computing in cardiology conference 2011, Hangzhou, China
166
E. Brill