Development of machine learning methods for improved fluorescence-based monitoring of environmental contaminants in surface waters
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Li, Ziyu
issued: 2021-09-20T17:29:17Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: This research examined the use of fluorescence spectroscopy as a rapid contaminant detection method. Identifying and quantifying environmental contaminants in surface waters has always been challenging after transient events such as spills or storm events. Previous research has shown fluorescence spectroscopy to be a promising tool for continuous monitoring of water quality due to its real-time and sensitive detection capabilities. However, persistent challenges with fluorescence approaches remain and limit its use in contaminant monitoring. In this study, research was carried out to identify possible data analysis techniques that improve the separation of overlapping fluorescence signals from background noise to accurately identify and quantify contaminants of interest. Algorithm-based dimensionality reduction methods, including parallel factor analysis (PARAFAC), principal component analysis (PCA), and autoencoder neural networks, were compared to a basic peak-picking method. It was observed that contaminant detection was most accurate using dimensionality reduction methods that maximally explain dataset variance, such as PCA, although at the cost of component interpretability. Due to the interference from background organic matter and other environmental impacts on fluorescence signals, fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources for calibration. The novel application of data processing techniques was investigated to enable the transfer of fluorescence detection models from one water source to another. A contaminant detection model from a relatively consistent and low organic background source was successfully transferred to a second source with higher organic concentration and distinct water quality. Only a few additional fluorescence spectra of the background water quality and contaminants of interest were required to successfully transfer the model without the need for labelled samples…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/79739
note: DEVELOPMENT OF MACHINE LEARNING METHODS FOR IMPROVED FLUORESCENCE-BASED MONITORING OF ENVIRONMENTAL CONTAMINANTS IN SURFACE WATERS by Ziyu Li B. Eng, Hohai University, 2018 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) September 2021 © Ziyu Li, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: DEVELOPMENT OF MACHINE LEARNING METHODS FOR IMPROVED FLUORESCENCE-BASED MONITORING OF ENVIRONMENTAL CONTAMINANTS IN SURFACE WATERS submitted by Ziyu Li in partial fulfillment of the requirements of the degree of Master of Applied Science . Dr. Nicolás Peleato, School of Engineering Supervisor Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Deborah Roberts, University of Northern British Columbia Supervisory Committee Member Dr. Mohammad Zarifi, School of Engineering University Examiner iii Abstract This research examined the use of fluorescence spectroscopy as a rapid contaminant detection method. Identifying and quantifying environmental contaminants in surface waters has always been challenging after transient events such as spills or storm events. Previous research has shown fluorescence spectroscopy to be a promising tool for continuous monitoring of water quality due to its real-time and sensitive detection capabilities. However, persistent challenges with fluorescence approaches remain and limit its use in contaminant monitoring. In this study, research was carried out to identify possible data analysis techniques that improve the separation of overlapping fluorescence signals from background noise to accurately identify and quantify contaminants of interest. Algorithm-based dimensionality reduction methods, including parallel factor analysis (PARAFAC), principal component analysis (…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0402172
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/2429/79739