Introducing CESWI 8
Published on: 14 Nov 2023
It's #TechnicalTuesday and Austen Buck is exploring how artificial neural networks can help us manage future microbial challenges in drinking water.
I’m still traumatised by the time my year 7 maths teacher confiscated my Tamagotchi. He didn’t feed it, or clean up its mess, and it sadly died at the ripe old age of 20 days (that’s 20 years in human years). At the time, I was fascinated by how these toys worked, both in terms of the operating system and the electronic hardware behind it, so much so I used to take them apart to the dismay of my parents. As a kid, it was easy to begin to wonder if the little beastie on the screen was somehow sentient. Fortunately, it wasn’t…. I hope!
Technology has made incredible progress since the time of virtual pets and artificial intelligence (AI) has become mainstream from its initial inception in the 1940s with work into artificial neurons by McCulloch and Pitts. Artificial neural networks (ANN), a core component of AI, provides the brain-inspired networks that feed machine learning (ML) algorithms for AI to solve tasks.
This image was created in under 10 seconds by an AI image generator tool using only 3 keywords: robot hitting bacteria.
ANN has been used successfully in multiple applications such as stock exchange prediction, handwriting recognition, fraud detection, automated car systems, and chemical plant control, but its use in the protection of public water supplies is relatively new, especially in the field of microbial control.
An exciting study by Miao et al, 2022, investigated waterborne disease prevalence in urban areas and demonstrated how multiple linear regression models have been used in conjunction with ANN to accurately predict enteric pathogen concentrations, such as norovirus and rotavirus, in surface waters using physicochemical parameters alone. Typically, collecting and analysing water samples for microbial parameters can take a minimum of 24h but with techniques such as these, instant and accurate predictions can be made in the absence of real-time microbial concentrations.
If we can harness the vast amounts of telemetry data collected on water and wastewater treatment works in conjunction with ANN the possibilities are significant:
There’s also nothing limiting the use of ANN to potable water supply and significant advantages could further be levered in catchment management, wastewater treatment, cooling systems and industrial processes both for microbial and physicochemical risk management. For instance, live “traffic light” style warning systems could be implemented at bathing waters to inform bathers of the microbial risks based on recent weather patterns and local river flows.
The water sector currently has multiple priorities that need addressing and whilst the solution to many of these problems are traditional ones, ANN should be considered as a potential solution now and a priority for future investment.
In commemoration of my Tamagotchi that lost its life, and the future billions of microbes that might be up against systems controlled by artificial neural networks. RIP.
Principal Consultant (Microbiology)
Austen has held numerous positions in the water industry over the last 15 years, always with a focus on microbiology and water safety planning. He currently heads up WRc’s Microbiology Team and leads and supports on international and national projects with water suppliers, government departments and regulators regarding microbiological monitoring, risk assessment, management and control, and water safety planning. He holds a PhD in Public Health and Environmental Microbiology and has co-authored publications on virus and bacteriophage removal through membrane bioreactor technologies. Austen is a Chartered Scientist with the British Science Council, a corporate member of the Institute of Water (IWater), and an area committee member and mentor within IWater.