IoT-Driven Water Quality Management System using Deep Q-Network

The ongoing process of digital transformation is revolutionizing organizations across various sectors, leading to significant operational changes. This study introduces a novel IoT-based water quality management system, powered by advanced machine learning algorithms, to ensure the safety and well-being of individuals within organizations. The proposed system deploys strategically positioned IoT sensors within the water control center, continuously monitoring water quality parameters. These sensors detect a range of harmful substances, including chlorides, sulfates, nitrates, nitrites, heavy metals, iron, manganese, and hardness minerals. When pollutant levels exceed predefined thresholds, a Deep Q-Network (DQN) algorithm is activated to assess the severity of the situation. By embracing digital transformation, organizations can enhance their ability to mitigate health risks and foster safer working environments. The system’s real-time monitoring, proactive alert mechanisms, and swift response capabilities showcase the advantages of leveraging digital technologies in revolutionizing water quality management practices. This study contributes to the ongoing digital transformation journey by demonstrating the practical implementation of smart, self-regulating systems to ensure water safety in organizational settings.