Water quality research: how Charles Sturt is using AI

Charles Sturt University researchers are using innovative technologies to improve water quality, including artificial intelligence (AI) and data mining. They’re conducting data-driven research to revise a water quality index (WQI). The results will benefit communities in Australia and around the world.

The research is led by Associate Professor Azizur Rahman. He is the Charles Sturt Statistics and Data Mining Research Group leader. He is conducting the project alongside Charles Sturt colleagues, and the National University of Ireland.

Water Research, the leading journal in the domain of water science and technology, published the research team’s paper, ‘A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment’.

Addressing poor water quality

The research addresses issues that have the potential for global impact. Surface water quality poses significant environmental, sociological and economic risks in many parts of the world, including Australia.

Pollution events can happen in many ways. For example, water events such as rain, winds, and nearby farming and/or mining can affect local river or surface water quality. Similarly, floods or shipping activities can affect harbour water quality. Azizur sheds light on some of the challenges.

“Sustainable management of water resources has become a challenge of critical importance. Surface water plays a key role in environmental ecosystems. So 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. This also includes health and epidemiological risks.”

“Due to population growth, industrialisation and urbanisation observed over many decades, freshwater usage and wastewater production have significantly increased. Both human activities and natural processes have caused a continuous degradation of surface water quality in recent decades.”

Drawing on new technologies

Many countries have adopted policies and guidelines to manage surface water quality and provide more effective water resource management to reverse negative trends.

“The research addresses some of these significant issues with an adaptation of AI in this data science era, especially for assessing coastal water quality. In recent years, moreover, a range of tools and techniques have been developed to assess the quality of surface waters. They can also diagnose the health of aquatic ecosystems. However, 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. 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.”

Making a difference in water quality

The updated WQI model and its information outputs can be used to benefit a range of local, state, national and international agencies. These include:

Local – local river and lake management authorities, farmers using river water for irrigation
State – NSW Water, NSW Environment, Dam Management, River Management, and the Sydney Harbour Authority
National – Murray Darling Basin Authority and Great Barrier Reef Management Authority
International – the European Union Water Framework Directive, integrated river basin management organisations. Plus, other comparable international water management institutes and organisations.