Researchers are using new tools to monitor the quality of water in streams and rivers that feed the Great Barrier Reef.

While existing water-quality sensors have provided near real-time data for science, their data is often affected by technical anomalies.

“Current automated water-quality monitoring systems can give us a faulty readout because rocks, organic debris, or living organisms like fish can block the probe’s lens path,” says Dr Edgar Santos-Fernandez from QUT’s School of Mathematical Sciences.

“To counter this, we have developed a new system that continuously detects and corrects errors and anomalies in the data from water-quality sensors to significantly improve the reliability of that data.

“It reduces uncertainties and biases in reporting and modelling while also decreasing the need for manual data annotation.”

By examining patterns across time and space, this method can differentiate between genuine water-quality changes and sensor malfunctions.

“After investigating the effectiveness of several state-of-the-art statistical methods, we developed this new dynamic approach for data streams that continuously updates as we get new data,” Dr Santos-Fernandez said.

“We validated this approach through a simulation study and real-world case study on Queensland’s Herbert River, which flows into the Great Barrier Reef.”

It is important to know exactly what is happening in the systems that contribute to the Reef. 

“The Reef’s resilience and biodiversity is threatened by land run-off of pollutants, such as fine sediment, excess nutrients and pesticides, into the many rivers and streams that flow into the Reef,” Dr Santos-Fernandez said. 

The new method could be applied more broadly to address growing challenges to global water quality.

“This innovative approach offers a valuable tool for scientists and policymakers to make more informed decisions about our vital water resources,” said principal investigator Professor Kerrie Mengersen, director of QUT’s Centre for Data Science.

More details are accessible here.

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