Behold the remarkable ways in which machine learning can improve water sanitation systems while bettering the lives of billions.
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The latest UNICEF report on global clean water and sanitation suggests that the impacts of COVID-19 were considerably higher on the urban areas, which don’t have access to clean water. That number is as high as 40% of the world population. The report also unveils that more than half of the world’s population (4.5 billion) doesn’t have access to a safe sanitation system.
Here at home a recent survey conducted by SUEZ – Water Technologies & Solutions found that Americans are increasingly alarmed about water scarcity, period, with 74% of those polled insisting more conserving action needs to be taken, within their communities.
Innovation and, more specifically, artificial intelligence (AI) might have the potential to assist further and resolve some challenges in sanitation system processes.
Emergency events and better delivery
By harnessing AI’s power along with big data, water utilities can maximize data interpretations and utilization to make better-informed decisions toward enhancing service delivery and reducing costs.
For instance, take Flint, Michigan, where the machine-learning algorithms model showed promising results by producing a list of properties suspected of having lead pipes with a 97% success rate. However, the project was later discarded by the city itself.
The global home water filtration unit market size was valued at $8.6 billion in 2018, but with the global, pandemic-induced stay-at-home orders, the market has gained immense traction, especially in urban areas. Many households are moving towards the whole house filtering systems such as Culligan, Aquasana, Sweetwater or the newly entered Samsung BESPOKE. By implementing AI, water filtration builders can examine consumers’ data and send notifications to their devices when the purifiers require replacement.
All substances dispersed in wastewater can have varying toxicity levels and must be treated appropriately to minimize their environmental impact. Wastewater treatment plants must use technologies that provide real-time effluent levels at remote wastewater treatment plants to manage them effectively.
Artificial intelligence and machine learning can help make more efficient decisions.
Early adopters of AI are quickly leaving reactive asset maintenance behind and machine learning can accurately predict information necessary to what, and when, assets need to be serviced, resulting in significant system availability. We can only be sure that this is just the beginning of implementing such technologies in industries that profoundly affect our everyday health and well-being.