Using Probabilistic Neural Networks to Analyze First Nations’ Drinking Water Advisory Data
Publication: Journal of Water Resources Planning and Management
Volume 144, Issue 11
Abstract
Drinking water advisories (DWAs) are a major issue facing many First Nations communities across Canada. This paper analyzes drinking water system data matched to DWA data using a probabilistic neural network (PNN) model to find key factors that influence the occurrence, frequency, duration, and cause of DWAs. First, for all data across Canada and subsequently for a number of data sets for individual provinces, the analyses were completed using an ensembles approach of running the same data set multiple times (in this case, five) to ensure that factors identified were representative of the entire data set and not just the training set. The results were compared with those from previous studies that used data mining techniques. Accuracies above 74% were achieved and key factors influencing each of the models were identified. The PNN models are a practical and powerful diagnostic tool for identifying key system attributes influencing DWAs, which can then be used to develop effective targeted solutions to mitigate the core issues that result in DWAs. These types of models can be used in other applications to identify influential factors and guide decision makers.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was made possible thanks to funding from the Natural Sciences and Engineering Research Council and Res’Eau, a National Centre of Excellence on small drinking water systems. Special thanks also go to Health Canada, the BC FNHA, and the CBC for providing additional data.
References
Allaire, M., H. Wu, and U. Lall. 2018. “National trends in drinking water quality violations.” PNAS 115 (9): 2078–2083. https://doi.org/10.1073/pnas.1719805115.
Asnaashari, A., E. A. McBean, B. Gharabaghi, and D. Tutt. 2013. “Forecasting watermain failure using artificial neural network modeling.” Can. Water Resour. J. 38 (1): 24–33. https://doi.org/10.1080/07011784.2013.774153.
Asnaashari, A., E. A. McBean, I. Shahrour, and B. Gharabaghi. 2009. “Prediction of watermain failure frequencies using multiple and Poisson regression.” Water Sci. Technol. Water Supply 9 (1): 9–19. https://doi.org/10.2166/ws.2009.020.
Azadeh, A., N. Neshat, and H. Hamidipour. 2012. “Hybrid fuzzy regression-artificial neural network for improvement of short-term water consumption estimation and forecasting in uncertain and complex environments: Case of a large metropolitan city.” J. Water Resour. Plan. Manage. 138 (1): 71–75. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000152.
Bascil, M., and H. Oztekin. 2012. “A study of hepatitis disease diagnosis using probabilistic neural network.” J. Med. Syst. 36 (3): 1603–1606. https://doi.org/10.1007/s10916-010-9621-x.
Borovinskiy, V. 2009. “Classification of iris data set.” Accessed May 22, 2017. http://labs.fs.uni-lj.si/lasin/wp/IMIT_files/neural/doc/seminar8.pdf.
Catterson, V. 2014. “Understanding data science: Classification with neural networks in R.” Accessed May 22, 2017. http://cowlet.org/2014/01/12/understanding-data-science-classification-with-neural-networks-in-r.html.
El Emary, I. M., and S. Ramakrishnan. 2008. “On the application of various probabilistic neural networks in solving different pattern classification problems.” World Appl. Sci. J. 4 (6): 772–780.
Ghiassi, M., D. Zimbra, and H. Saidane. 2008. “Urban water demand forecasting with a dynamic artificial neural network model.” J. Water Resour. Plan. Manage. 134 (2): 71–75. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(138).
Government of Canada. 2017. “Drinking water advisories: First Nations south of 60.” Accessed May 24, 2017. https://www.canada.ca/en/health-canada/topics/health-environment/water-quality-health/drinking-water/advisories-first-nations-south-60.html.
Guyon, I. 1996. “A scaling law for the validation-set training-set size ratio.” Accessed May 3, 2017. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.33.1337&rep=rep1&type=pdf.
Hansen, L. K., and P. Salamon. 1990. “Neural network ensembles.” IEEE Trans. Pattern Anal. Mach. Intell. 12 (10): 993–1001. https://doi.org/10.1109/34.58871.
Harvey, R., E. McBean, and B. Gharabaghi. 2014. “Predicting the timing of water main failure using artificial neural networks.” J. Water Resour. Plann. Manage. 140 (4): 425–434. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000354.
Harvey, R., H. M. Murphy, E. A. McBean, and B. Gharabaghi. 2015. “Using data mining to understand drinking water advisories in small water systems: A case study of Ontario First Nations drinking water supplies.” Water Resour. Manage. 29 (14): 5129–5139. https://doi.org/10.1007/s11269-015-1108-6.
Health Canada. 2014. “Guidance for issuing and rescinding boil water advisories.” Accessed May 22, 2017. http://www.hc-sc.gc.ca/ewh-semt/consult/_2014/boil_water-eau_ebullition/consult-eng.php.
INAC (Indigenous and Northern Affairs Canada). 2000. “Band classification manual.” Accessed May 25, 2017. http://publications.gc.ca/collections/Collection/R22-1-2000E.pdf.
INAC (Indigenous and Northern Affairs Canada). 2017. “First nation profiles.” Accessed July 25, 2018. http://fnp-ppn.aandc-aadnc.gc.ca/fnp/Main/index.aspx?lang=eng.
INAC (Indigenous and Northern Affairs Canada). 2016. “National assessment of First Nations water and wastewater systems–2009-2011.” Accessed May 22, 2017. https://www.aadnc-aandc.gc.ca/eng/1313426883501/1313426958782.
Kaur, J. 2017. “Overview of artificial neural networks and its applications.” Accessed May 17, 2017. https://www.xenonstack.com/blog/overview-of-artificial-neural-networks-and-its-applications.
Kavzoglu, T. 2009. “Increasing the accuracy of neural network classification using refined training data.” Environ. Modell. Software 24 (7): 850–858. https://doi.org/10.1016/j.envsoft.2008.11.012.
Levasseur, J., and J. Marcoux. 2015. “Bad water: ‘Third world’ conditions on First Nations in Canada.” Accessed May 17, 2017. http://www.cbc.ca/news/canada/manitoba/bad-water-third-world-conditions-on-first-nations-in-canada-1.3269500.
Leverington, D. 2009. “A basic introduction to feedforward backpropagation neural networks.” Accessed May 5, 2017. http://www.webpages.ttu.edu/dleverin/neural_network/neural_networks.html.
Looney, C. G. 1996. “Advances in feed-forward neural networks: Demystifying knowledge acquiring black boxes.” IEEE Trans. Knowl. Data Eng. 8 (2): 211–226. https://doi.org/10.1109/69.494162.
Luzanin, O., and M. Plancak. 2014. “Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network.” Assembly Autom. 34 (1): 94–105. https://doi.org/10.1108/AA-03-2013-020.
Murphy, H. M., M. Bhatti, R. Harvey, and E. A. McBean. 2016. “Using decision trees to predict drinking water advisories in small water systems.” J. Am. Water Works Assoc. 108 (2): E109–E118. https://doi.org/10.5942/jawwa.2016.108.0008.
Myles, A. J., R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown. 2004. “An introduction to decision tree modeling.” J. Chemom. 18 (6): 275–285. https://doi.org/10.1002/cem.873.
Odan, F. K., and L. F. Reis. 2012. “Hybrid water demand forecasting model associating artificial neural network with Fourier series.” J. Water Resour. Plan. Manage. 138 (3): 245–256. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000177.
Sattar, A., O. F. Ertugrul, B. Gharabaghi, E. McBean, and J. Cao. 2017. “Extreme learning machine model for water network management.” Neural Comput. Appl. 1–17. https://doi.org/10.1007/s00521-017-2987-7.
Sattar, A., B. Gharabaghi, and E. McBean. 2016. “Prediction of timing of watermain failure using gene expression models.” Water Resour. Manage. 30 (5): 1635–1651. https://doi.org/10.1007/s11269-016-1241-x.
Smith, J., and R. N. Eli. 1995. “Neural-network models of rainfall-runoff process.” J. Water Resour. Plan. Manage. 121 (6): 499–508. https://doi.org/10.1061/(ASCE)0733-9496(1995)121:6(499).
Swingler, K. 1996. Applying neural networks: A practical guide. San Diego: Academic Press.
Thompson, E. 2016. “Investigating drinking water advisories in First Nations communities through data mining.” Masters thesis. Accessed May 1, 2017. http://hdl.handle.net/10214/10021.
United Nations. 2009. “State of the world’s indigenous peoples.” Accessed May 3, 2017. http://www.un.org/esa/socdev/unpfii/documents/SOWIP/en/SOWIP_web.pdf.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 144 • Issue 11 • November 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Aug 23, 2017
Accepted: May 9, 2018
Published online: Aug 16, 2018
Published in print: Nov 1, 2018
Discussion open until: Jan 16, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.