Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer. The research is published in the journal PLOS Water.
Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer. The research is published in the journalAs drinking water is not piped into homes in most settlements, residents instead collect it from public tap stands using storage containers.
Using machine learning, the research team—including Associate Professor Usman Khan, also of Lassonde—has developed a new way to predict the probability that enough chlorine will remain until the last glass is consumed. They used an artificial neural network along with ensemble forecasting systems , something that is not typically done. EFS is a probabilistic model commonly used to predict the probability of precipitation in weather forecasts.
Factors such as local temperature, how the water is stored and handled from home to home, the type and quality of the water pipes,and whether a child dipped their hand in the water container can all play a role in how safe the water is to drink. The researchers used routine water quality monitoring data from two refugee settlements in Bangladesh and Tanzania collected through the Safe Water Optimization Tool Project. In Bangladesh, the data was collected from 2,130 samples by Médecins Sans Frontières from Camp 1 of the Kutupalong-Balukhali Extension Site, Cox's Bazaar between June and December 2019 when it hosted 83,000 Rohingya refugees from neighboring Myanmar.
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