Open Collections

Zhang, Yirao

University of British Columbia

This research investigated the use of Bayesian methods in predictive modelling of cyanobacteria concentration and Cryptosporidium presence/absence. Cyanobacteria blooms and Cryptosporidium spp. in source waters are ubiquitous concerns for water treatment and management. However, identifying and enumeration cyanobacteria and Cryptosporidium has always been challenging. Previous research has shown Bayesian methods to be a promising approach to predicting water quality variables with uncertainty. However, the innate nature of highly imbalance in cyanobacteria abundance and Cryptosporidium classification data reduces the performance of traditional predictive models. Moreover, few studies have focused on the probabilistic disease burden estimation of Cryptosporidium after prediction. In this study, a Bayesian approach was proposed to fit the cyanobacteria abundance data with mixture models that handle zero-inflated data. Predictor variables considered included weather and water quality measures that can easily be obtained day-to-day. Several models were compared based on fit to training data. Furthermore, the optimal model (zero-inflated negative binomial) was used to predict cyanobacteria alert levels on a separated test set of data. The ability to predict narrow alert levels was limited. However, high accuracy was achieved in predicting cyanobacteria counts above or below 1,000 cells/mL. For Cryptosporidium, a probabilistic quantitative microbial risk assessment approach was proposed to predict Cryptosporidium presence/absence and estimate the disease burden presented in disability-adjusted life years (DALYs). For severely imbalanced Cryptosporidium data, the model achieved high precision and recall. The probabilistic QMRA based on Monte Carlo and Markov chain Monte Carlo method was used to estimate disease burden under different scenarios and backwards infer the necessary level of treatment and critical control points for sewer overflow. The modeling approach can be applied to assess risk under different scenarios and advice for water management.

Text

2022-08-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82241

Civil Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/