ARDC Research Link Australia

ORCID Profile
Orcid icon. 0000-0002-3494-8655

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Research Topics

In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.

ANZSRC Field of Research (FoR)

ANZSRC Socio-Economic Objective (SEO)

Environmental Management Systems | Climate Change Adaptation Measures | Physical and Chemical Conditions of Water for Urban and Industrial Use | Physical and Chemical Conditions of Water in Fresh, Ground and Surface Water Environments (excl. Urban and Industrial Use) | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management | Water Recycling Services (incl. Sewage and Greywater) | Water Services and Utilities

Publications

Publication

Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms

Publisher: IWA Publishing

Date: 2020

DOI: 10.2166/H2OJ.2020.035

Abstract: The present understanding of how changes in climate conditions will impact the flux of natural organic matter (NOM) from the terrestrial to aquatic environments and thus aquatic dissolved organic carbon (DOC) concentrations is limited. In this study, three machine learning algorithms were used to predict variations in DOC concentrations in an Australian drinking water catchment as a function of climate, catchment and physical water quality data. Four independent variables including precipitation, temperature, leaf area index and turbidity (n = 5,540) were selected from a large dataset to develop and train each machine learning model. The accuracy of the multivariable linear regression, support vector regression (SVR) and Gaussian process regression algorithms with different kernel functions was determined using adjusted R-squared (adj. R2), root-mean-squared error (RMSE) and mean absolute error (MAE). Model accuracy was very sensitive to the time interval used to average climate observations prior to pairing with DOC observations. The SVR model with a quadratic kernel function and a 12-day time interval between climate and water quality observations outperformed the other machine learning algorithms (adj. R2 = 0.71, RMSE = 1.9, MAE = 1.35). The area under the receiver operating characteristic curve method (AUC) confirmed that the SVR model could predict 92% of the elevated DOC observations however, it was not possible to estimate DOC values at specific s ling sites in the catchment, probably due to the complex local geological and hydrological changes in the sites that directly surround and feed each s ling point. Further research is required to establish potential relationships between climatological data and NOM concentration in other water catchments – especially in the face of a changing climate.

Publication

Management of biogenic taste and odour: From source water, through treatment processes and distribution systems, to consumers

Publisher: Elsevier BV

Date: 12-2022

DOI: 10.1016/J.JENVMAN.2022.116225

Abstract: Biogenic taste and odour (T&O) have become a global concern for water utilities, due to the increasing frequency of algal blooms and other microbial events arising from the combined effects of climate change and eutrophication. Microbially-produced T&O compounds impact source waters, drinking water treatment plants, and drinking water distribution systems. It is important to manage across the entire biogenic T&O pathway to identify key risk factors and devise strategies that will safeguard the quality of drinking water in a changing world, since the presence of T&O impacts consumer confidence in drinking water safety. This study provides a critical review of current knowledge on T&O-causing microbes and compounds for proactive management, including the identification of abiotic risk factors in source waters, a discussion on the effectiveness of existing T&O barriers in drinking water treatment plants, an analysis of risk factors for biofilm growth in water distribution systems, and an assessment of the impacts of T&O on consumers. The fate of biogenic T&O in drinking water systems is tracked from microbial production pathways, through the release of intracellular T&O by cell lysis, to the treatment of microbial cells and dissolved T&O. Based on current knowledge, five impactful research and management directions across the T&O pathway are recommended.

Related Organisations

No related organisations have been discovered for Lisa Hamilton.

Related Funding Activities

Grant

Linkage Projects - Grant ID: LP160100620

Start Date: 06-2017

End Date: 06-2021

Amount: $450,000.00

Funder: Australian Research Council

View Funded Activity

Grant

Industrial Transformation Training Centres - Grant ID: IC190100031

Start Date: 08-2020

End Date: 08-2025

Amount: $3,973,202.00

Funder: Australian Research Council

View Funded Activity

Grant

Linkage Projects - Grant ID: LP130100033

Start Date: 02-2014

End Date: 11-2017

Amount: $390,000.00

Funder: Australian Research Council

View Funded Activity

Lisa Hamilton

Researcher

Research Topics

Top 5 Research Topics

ANZSRC Field of Research (FoR)

ANZSRC Socio-Economic Objective (SEO)

Publications

Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms

Management of biogenic taste and odour: From source water, through treatment processes and distribution systems, to consumers

Related Organisations

Related Funding Activities

Linkage Projects - Grant ID: LP160100620

Industrial Transformation Training Centres - Grant ID: IC190100031

Linkage Projects - Grant ID: LP130100033

Lisa Hamilton

Researcher

Research Topics

Top 5 Research Topics

ANZSRC Field of Research (FoR)

ANZSRC Socio-Economic Objective (SEO)

Related Links

Publications

Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms

Management of biogenic taste and odour: From source water, through treatment processes and distribution systems, to consumers

Related Organisations

Related Funding Activities

Linkage Projects - Grant ID: LP160100620

Industrial Transformation Training Centres - Grant ID: IC190100031

Linkage Projects - Grant ID: LP130100033

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