Application of WEKA Machine Learning Tools in Phytoremediation for the Removal of Heavy Metals in Water Treatment Processes

In this study, we leverage the interdisciplinary strengths of Computer Science, AI, ML, and Data Mining to address the pressing environmental issue of river pollution, with a focus on the Yamuna River's deteriorating conditions. Recognizing the critical impact of heavy metal contamination in aquatic ecosystems, our research utilizes an AI-enhanced phytoremediation strategy for the efficient reduction of both physicochemical properties and hazardous heavy metals from the river water. We perform a thorough examination of different physicochemical factors including pH, electrical conductivity, total dissolved solids, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), and the levels of metals such as lead (Pb), zinc (Zn), and nickel (Ni). To process and interpret the complex dataset, we apply sophisticated Data Mining techniques using the software WEKA, which enables us to extract meaningful patterns and insights from the environmental data. Our approach adopts a green technology method, phytoremediation, enhanced by AI algorithms to optimize the removal process, targeting the reduction of contaminants in a cost-effective and scalable manner. The integration of AI and Data Mining in environmental management signifies a transformative step towards more intelligent, efficient, and sustainable practices. The implications of this research are manifold, extending beyond environmental science to inform developments in AI-driven ecological modeling and ML-powered monitoring systems. The outcomes of this study not only shed light on the current state of the Yamuna River but also demonstrate the potential of AI and Data Mining as powerful tools in the fight against environmental pollution