by Charles Sturt University
A Charles Sturt University researcher and colleagues have used artificial intelligence (AI) and data mining to revise a water quality index (WQI) which can benefit individuals and a range of government agencies and non-government entities.
A Charles Sturt University researcher and colleagues have used artificial intelligence (AI) and data mining to revise a water quality index (WQI) which can benefit individuals and a range of government agencies and non-government entities.
The research is by Associate Professor in Mathematics and Statistic Azizur Rahman, Leader of the Statistics and Data Mining Research Group in the Charles Sturt School of Computing, Mathematics and Engineering, and colleagues, and the National University of Ireland.
The research paper ‘A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment’ has been published in Water Research, the leading journal in the domain of Water Science and Technology.
Professor Rahman said the research was necessary because surface water quality poses significant environmental, sociological, and economic risks in many parts of the world, including Australia.
“Sustainable management of water resources has become a challenge of critical importance,” he said.
“Surface water plays a key role in environmental ecosystems, and if we ignore advanced developments which can be applied to the assessment process, it will pose significant risks to our societies and the natural environments, including health and epidemiological risks.”
He said pollution events can happen in many ways; for example, local river or surface water quality can be affected by quick water events such as rain, winds, and even nearby farming and/or mining activities, and similarly, harbour water quality can be affected by floods, or shipping activities.
The researchers applied their state-of-the-art model to Cork Harbour, in Ireland, which is perhaps similar to many Australian harbours (e.g. Sydney Harbour, or Port Phillip Bay in Melbourne).
Professor Rahman said while society relies on existing water quality standards, these need to be updated because the social, economic, human activities and environmental circumstances and changes are a dynamic process, and water quality standards need to be assessed dynamically on a regular basis with the integration of latest data and adaptation of robust technologies.
“Due to population growth, industrialisation and urbanisation observed over many decades, freshwater usage and wastewater production have significantly increased, and both human activities and natural processes have caused a continuous degradation of surface water quality in recent decades,” Professor Rahman said.
Professor Rahman said many countries have adopted a range of policies and guidelines to manage surface water quality and provide more effective water resource management to reverse this negative trend.
“This research was necessary to address some of these significant issues with an adaptation of AI in this data science era, especially for assessing coastal water quality,” he said.
“Although in recent years a range of tools and techniques have been developed to assess the quality of surface waters and diagnose the health of aquatic ecosystems, there are issues in the model structures, applications, sources of model uncertainty and eclipsing problems.
“Our AI-integrated WQI model can indicate, for example, any pollutants in the water which are crucially important for the health of the Great Barrier Reef, and thus the surface water quality assessment could be used to improve intervention-related outcomes such as better health of the reef through improved ecosystem and water.”
Professor Rahman said this updated WQI model and its valuable and reliable information outputs could be used by and benefit a range of local, state, national and international agencies. These include:
- Local – local river and lake management authorities, farmers using river water for irrigations
- State – NSW Water, NSW Environment, Dam Management, River Management, the Sydney Harbour Authority
- National – Murray Darling Basin Authority, Great Barrier Reef Management Authority
- International – the European Union Water Framework Directive, integrated river basin management organisations, and other comparable international water management institutes and organisations.
Reference (open access):
Uddin MG, Nash S, Rahman A, and Olbert AI (2022). A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Research, 219, 1-20. [118532]. https://doi.org/10.1016/j.watres.2022.118532 https://www.sciencedirect.com/science/article/pii/S0043135422004857
