Assessing Water Quality Using Machine Learning

For all living things, water is one of the most crucial natural resources. The ability to access clean water is essential for human survival. Beyond drinking, water is also necessary for animal consumption, irrigation, as well as domestic and commercial uses. For the stability and conservation of the environment, it is crucial to monitor the quality of treated wastewater to protect the ecosystem. The issue of poor water quality is getting worse every day due to a number of factors & other dangerous chemicals. Laboratory assessments of water samples to determine their fitness to use is a vital step in the water quality assurance process. However, laboratory assessments require adherence to stringent measures, which might be difficult to comply with. Machine learning (ML) has emerged in recent years as viable and cheaper solutions to replace the lab-based assessments, with a limited availability of sufficient data to train the ML models. Unfortunately, such data is not always available, especially in less developed countries. So that the work attempts to fill this gap by creating sample sized datasets that can be used to train & test the ML models. In simple words the datasets were curated by aggregating data from smaller datasets on related concepts, then processed and labeled to make them useful for supervised ML models.