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Title: Application of Clustering Approach for Risk Assessment of Drinking Water Facilities Worldwide
United Nations recognized access to safe drinking water as a human right, yet many countries in the developing world lack access to potable water. Recurrent incidences of water-borne illnesses have a devastating effect on the morale and personal well-being of many people living in developing countries, contrasting the achievement of the UN’s objective. Qualitative and semi-quantitative approaches used for risk assessment are often ineffective, time-consuming, and do not discern the risk due to ingestion of unsafe drinking water at the global scale. This research utilizes a global dataset of drinking water facilities to evaluate the risks using a clustering approach. Extensive data analysis involving predetermined risk thresholds, the exceedance of which indicates the potential adverse risk. These risk-thresholds are based on the JMP Service Ladder, which effectively utilizes density-based spatial clustering of applications. Risk analysis of 132 datasets was conducted to designate the risk categories ranging from low, medium, and high-risk. Of the dataset analyzed, 90 areas were designated as a low-risk category while 42 were medium-risk. Overall, the clustering approach is an excellent tool to analyze a large dataset for risk assessment which will help the potential stakeholder, including the water utility manager, to assess the potential risk due to declining water quality quickly. Additionally, the clustering approach can be further harnessed for better data visualization, long-term performance evaluation of water utility, and real-time drinking water quality monitoring.  more » « less
Award ID(s):
1832536
NSF-PAR ID:
10358972
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 39th IAHR World Congress
Page Range / eLocation ID:
3574 to 3579
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. 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New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 
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