Approximately 2.2 billion people worldwide lack access to safe, managed drinking water. In addition, nearly half of the world’s population experiences severe water shortages at some point each year.
To address these pressing problems, significant socio-economic investments are being made in alternatives such as sewer irrigation, rainwater reuse and seawater desalination.
However, centralized water distribution systems often struggle to quickly adapt to fluctuating water demand, which is driving interest in decentralized water production technologies.
Researchers from the Korea Institute of Science and Technology (KIST), led by Dr. Son Moon, have developed an innovative solution that uses data-driven artificial intelligence to improve electrochemical water purification processes.
In collaboration with the team of Professor Baek Sang-Soo at Yeongnam University, they created a random forest model, an efficient machine learning technique, to predict ion concentrations in water more accurately than existing sensors that rely on electrical conductivity measurements.
The new AI model showed impressive accuracy, achieving an R² value of approximately 0.9 for predicting the concentrations of key ions such as sodium, potassium, calcium and chloride.
This breakthrough enables real-time monitoring of water quality, which is essential for effective water resource management.
The researchers found that the model had to be updated every 20 to 80 seconds to maintain accuracy, indicating that frequent measurements are crucial for tracking specific ions.
Dr. Moon emphasized the importance of this research, not only for the development of AI technology, but also for its potential applications within national water quality management systems.
By enabling precise monitoring of individual ion concentrations, this technology can significantly improve societal water well-being and contribute to more effective responses to global water challenges.
As the world grapples with increasing water scarcity due to climate change and population growth, innovations like these offer hope for improved water management strategies that can be adapted to the needs of different communities.
