Exploratory Analysis of Water Quality in a Small Urbanized Watershed Using Deep Learning

Simple item page
dc.contributor.advisorErechtchoukova, Marina G.
dc.contributor.authorOfosu, Alfred
dc.date.accessioned2023-12-08T14:43:00Z
dc.date.available2023-12-08T14:43:00Z
dc.date.issued2023-12-08
dc.date.updated2023-12-08T14:43:00Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractWater is a life-sustaining resource for living organisms inside and outside water bodies. Natural waters serve as municipal and industrial water supplies, sources for agricultural irrigation, homes for aquatic ecosystems, recreation, and other essential uses. The quality of water determines its use. Therefore, it must be monitored, managed, and reported to help stakeholders in decision-making that can protect watershed ecosystems and improve measures to mitigate factors adversely affecting water bodies. Water quality is represented by a set of parameters that describe specific characteristics or properties of water. These parameters are determined by measuring water's physical and chemical characteristics and concentration levels of various substances in a water column with subsequent sample analysis in laboratories. This results in low frequencies of observations for water quality parameters compared to hydrometric and meteorological data. Frequencies of observation adopted by many water quality monitoring systems vary between 4 and 12 samples per year, suggesting applying modelling techniques to support decision-making. The study aims to develop a data-driven computational tool for water quality modelling in a small, highly urbanized watershed of the Don River, Ontario, Canada. The study focuses on major ions, namely, cations: calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+), and anions such as bicarbonate (HCO3-), carbonate (CO32-), chloride (Cl-), and sulphate (SO42-). These parameters are not affected significantly by the aquatic ecosystem. The hydrological and meteorological processes mainly determine their dynamics. The study uses data from different monitoring systems belonging to the Toronto and Region Conservation Authority (TRCA) and Environment and Climate Change Canada (ECCC). It consists of water quality parameters and hydrometric and meteorological characteristics observed in the watershed over 57 years. Concentrations of selected water quality parameters are modelled using deep neural networks. The data pre-processing framework for cleansing and integrating data observed at different frequencies from different locations is developed. The framework is applied for the comparative analysis of neural networks of various configurations. Two sets of computational experiments were conducted. In the first set of experiments, integrated data from all monitoring stations in the watershed was fed into the deep learning algorithms to train a neural network to predict the concentration of major ions for the upcoming month (t+1). The second set of experiments uses upstream environmental parameters to train the model and predict the major ion concentrations in the lower subwatershed. The study investigates the performance of developed models in accurately predicting ion concentrations and provides insights into the relationship between environmental factors and water quality in the investigated watershed. The findings have practical applications for water resource management and pollution prevention efforts.
dc.identifier.urihttps://hdl.handle.net/10315/41742
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subjectInformation science
dc.subjectHydrologic sciences
dc.subject.keywordsWater quality
dc.subject.keywordsIntegrated hydrology
dc.subject.keywordsMajor ions
dc.subject.keywordsMachine learning
dc.subject.keywordsDeep learning
dc.subject.keywordsImputation of missing values
dc.titleExploratory Analysis of Water Quality in a Small Urbanized Watershed Using Deep Learning
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ofosu_Alfred_2023_Masters.pdf
Size:
8.71 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Information Systems and Technology

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.