Neural network-based monitoring of critical parameters in drinking water for enhanced quality assurance

Abstract

Every nation has its own approved parameters for water quality standards provided by the World Health Organization (WHO). These parameters undergo variations based on geography. The Sultanate of Oman has listed prime important parameters for unbottled drinking water according to Oman Standards. Currently, the utilization of advanced Internet of Things (IoT) technology involves numerous internal measurements through multiple sensors. These sensors continuously monitor water quality indicators such as pH levels, turbidity, and microbial content, ensuring that the drinking water meets the stringent standards set by the Sultanate of Oman. This integration of IoT technology enhances real-time data collection and analysis, facilitating prompt responses to any deviations from the prescribed water quality parameters. However, determining the specific purpose of water usage is not yet defined. Traditionally, water quality is typically examined only for physiochemical properties. However, given that water is a colorless liquid, its suitability for drinking cannot be solely determined based on these properties. By employing Time Series Neural Networks, we can identify the current parameters of water and display or transmit information wirelessly as a report. This report would include a comparison between the actual value and the measured value using recent sensors and instrumentation. Additionally, the Time Series Neural Networks allow for real-time monitoring, enabling swift detection of anomalies or fluctuations in water parameters, contributing to proactive decision-making in water management. The seamless integration of advanced technology not only enhances the accuracy of data analysis but also facilitates timely responses to potential environmental changes or emerging issues.