Predicting Heavy Metal Ion Concentrations in Water to Prevent Future Neurological Outbreaks

This paper presents an integrated approach on water quality assessment including heavy metal and advanced learning techniques: support vector machine, decision tree pressure, random forest. This new approach would thus enable real-time monitoring and management of water resources, hence being a very effective tool for the solution of environmental problems through heavy metals. Water quality dynamics is achieved in order to be able to realize the early detection of reductions in pollution by adding integration of machine learning algorithms, and with the further incorporation of random forest, its robustness and adaptability in diverse environments can also be enhanced. This project makes some data important, highlighting their need to be put in use with a structured approach towards environmental health and water management. We address a variety of large-scale and cost-effective solutions to water pollution through a combination of metrics of advanced machine learning techniques for improvement in environmental protection as well as positive public health outcome.