Assessment of source water microbial quality using Bayesian belief networks and data balancing algorithms
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Aliashrafi Zagi, Atefeh
issued: 2022-05-27T19:21:52Z
issued: 2022
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Cryptosporidium and E. coli are recognized as critical pathogens in source water with mortality risk. In order to protect public health from waterborne risks, monitoring of Cryptosporidium and E. coli in drinking water sources is essential. However, direct measurement of these pathogens is expensive and labor-intensive, resulting in limited information and time-delays for risk-based management of water systems. While these challenges slow down the real-time monitoring of pathogens’ levels in ambient waters, AI-based techniques offer a fast and effective alternative for direct measurements. Bayesian Belief Networks (BBNs) is one of these data-driven methods gaining traction in modelling environmental systems and capturing their uncertainties. BBNs can assist the decision-makers by visualizing the interaction of variables in the complex systems. In this thesis, BBNs have been used to estimate Cryptosporidium and E. coli levels to provide a real-time assessment of the microbial quality of source water and fill the time gap required for direct measurement. However, available Cryptosporidium data are rare and unbalanced, mainly indicating absence or non-detectable levels of Cryptosporidium. To overcome this challenge, two data balancing algorithms, Adaptive Synthesized Sampling (ADASYN) and Synthetic Over Sampling Technique (SMOTE) have been utilized. The objective was to eliminate unbalanced features of the dataset and train the model in a way that can predict both presence and absence of Cryptosporidium based on unbalanced and real measurements. In current work, the BBN model has been used for Cryptosporidium prediction and trained for the first time with a balanced dataset generated through ADASYN and SMOTE algorithms. The application of balancing algorithms increased the prediction accuracy to more than 60%, compared with models developed by unbalanced datasets. Furthermore, the sensitivity of pathogen’s level to different water quality and weather parameters was als…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/81648
note: ASSESSMENT OF SOURCE WATER MICROBIAL QUALITY USING BAYESIAN BELIEF NETWORKS AND DATA BALANCING ALGORITHMS by Atefeh Aliashrafi Zagi B.Sc., University of Tabriz, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2022 © Atefeh Aliashrafi Zagi, 2022ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: ASSESSMENT OF SOURCE WATER MICROBIAL QUALITY USING BAYESIAN BELIEF NETWORKS AND DATA BALANCING ALGORITHMS submitted by Atefeh Aliashrafi Zagi in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Nicolas Peleato, School of Engineering Supervisor Dr. Cigdem Eskicioglu, School of Engineering Supervisory Committee Member Dr. Rehan Sadiq, School of Engineering Supervisory Committee Member Dr. Abbas Milani, School of Engineering University Examiner Click or tap here to enter text. iii Abstract Cryptosporidium and E. coli are recognized as critical pathogens in source water with mortality risk. In order to protect public health from waterborne risks, monitoring of Cryptosporidium and E. coli in drinking water sources is essential. However, direct measurement of these pathogens is expensive and labor-intensive, resulting in limited information and time-delays for risk-based management of water systems. While these challenges slow down the real-time monitoring of pathogens’ levels in ambient waters, AI-based techniques offer a fast and effective alternative for direct measurements. Bayesian Belief Networks (BBNs) is one of these data-driven methods gaining traction in modelling environmental systems and capturing their uncertainties. BBNs can assist the decision-makers by visualizing the interaction of variables in the complex systems. In this thesis, BBNs have been used to estimate Cryptospori…
has type: Thesis/Dissertation
date issued: 2022-09
is shown at: 10.14288/1.0413713
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Peleato, Nicolas M.
type: Text
identifier URI: http://hdl.handle.net/2429/81648