Artificial Intelligence Improves Water Quality Detection

Water quality is a major public health issue, threatened by industrial pollution and chemical discharges. A recent advancement uses machine learning methods to assess the safety of drinking water and identify risk factors. Thanks to a database containing nine key characteristics such as pH, hardness, dissolved solids, and turbidity, algorithms have been trained to distinguish between safe and dangerous water.

Among the tested techniques, the random forest method proved to be the most effective with an accuracy rate of 83.83%, significantly surpassing previous systems that achieved between 60% and 70%. This model not only classifies water as drinkable or not but also highlights the main parameter responsible for any potential contamination. For example, an excessively high sulfate level can cause digestive issues, while an excessive concentration of trihalomethanes is linked to cancer risks.

The originality of this approach lies in its accessibility. A web interface developed in Python with Flask allows users to obtain instant analysis. Simply enter the values of the various parameters to determine water quality and associated risks. This system is particularly useful in regions without analysis laboratories, where traditional tests are expensive and time-consuming.

Other evaluated algorithms, such as decision trees or gradient boosting methods, also showed good results but with slightly lower accuracy. However, their combined use enhances the overall reliability of the system. Random forests stand out for their ability to manage complex interactions between parameters, where simple threshold-based rules fail.

Water contains many substances that influence its quality. A pH that is too acidic or too basic irritates the skin and mucous membranes. Excessive hardness can promote pipe corrosion, releasing heavy metals. Dissolved solids in large quantities alter taste and can harm health. Conductivity, linked to ion concentration, reveals the presence of impurities. Finally, high turbidity can harbor pathogenic microorganisms, reducing the effectiveness of disinfectants like chlorine.

This work paves the way for real-time water quality monitoring, essential for preventing waterborne diseases such as cholera or typhoid. By quickly identifying sources of contamination, authorities can act more effectively to protect populations. Additionally, integrating connected sensors with this model would enable automatic and continuous detection, reducing the need for manual analysis.

The results demonstrate that machine learning can play a key role in water resource management. By combining accuracy, speed, and interpretability, this solution provides a practical tool for both citizens and policymakers, thereby contributing to the preservation of public health and the environment.

Sources and Credits

Source Study

DOI: https://doi.org/10.1007/s10791-026-09917-x

Title: Water quality detection and analysis using machine learning algorithms to assess the impact of influencing factors on health issues

Journal: Discover Computing

Publisher: Springer Science and Business Media LLC

Authors: Manikanta Manohar Pattisapu; Appala Naidu P; Ambeshwar Kumar; Avishek Nandi