From Measured pH to Hidden BOD: Quasi Real-Time Estimation of Key Indirect Water Quality Parameters Through Direct Sensor Measurements

Securing water quality is essential for environmental sustainability, public health, and the preservation of biodiversity. However, traditional water monitoring methods often fall short in providing real time, comprehensive insights. Our research introduces an innovative framework that exploits cutting-edge sensor technologies and the Internet of Things (IoT) for the real time and continuous monitoring of a broad array of water quality metrics. Our system adeptly measures direct parameters like pH, temperature, and dissolved oxygen, and innovatively employs machine learning to estimate traditionally unmeasurable parameters such as biochemical oxygen demand (BOD), nitrate-nitrogen, and phosphate concentrations. This estimation can be made possible using machine learning based algorithms on extensive data captured by IoT sensor network. The framework is complemented by sophisticated data visualization tools, providing intuitive, actionable insights. Our integrated approach ushers in a proactive paradigm for water resource management, promising significant advancements in environmental governance, sustainable development, and health policy, thereby establishing a new standard in IoT-enabled environmental monitoring.