Abstract:
This study investigates the use of machine learning methods to identify biological microbes in water. Biological contamination of water is a major global concern as it can cause water-borne illnesses and even death. Traditional methods for detecting microorganisms in water involve manual processes and are time-consuming and expensive. For automating this procedure and cutting the time and expense associated with detection, machine learning presents a promising solution. In this paper, we discussed the present status of research on the detection of biological microbes in water using machine learning approaches such artificial neural networks, support vector machines, and decision trees. In addition, we go over the drawbacks of this strategy and suggest potential remedies to address them. According to our research, machine learning has the power to transform the way biological microbes are detected in water and enhance public health results.
