Intelligent Real-Time Monitoring System for Wastewater Management Using Artificial Neural Network

The importance of real-time wastewater quality monitoring in addressing the increasing demand for water and the impact of climate change on wastewater treatment systems is discussed in this work. It highlights the potential of Artificial Intelligence (AI)-based solutions, specifically Artificial Neural Networks (ANNs), to accurately monitor water quality and manage wastewater effectively. Emphasis is placed on the integration of ANNs with the Kalman filter, a technique renowned for enhancing accuracy. This work presents the architecture of a wireless sensor network-based system designed to monitor wastewater quality. Sensor nodes strategically positioned and equipped with microcontrollers, sensors, and transceivers collect data, which undergoes processing using the Kalman filter to estimate and rectify any inaccuracies before transmission to the base station. The system employs multi-sensor data collection and ANN analysis to improve the accuracy and reliability of water quality monitoring. The system's performance is evaluated in terms of recognition and false alarm rates. It achieves a high recognition rate of 95.60% and a low false alarm rate, significantly increasing wastewater utilization efficiency. The ANN decision-making process is efficient, taking only 15 µs. The evaluation also includes the F1 score, demonstrating the system's classification abilities and overall performance. In conclusion, the proposed intelligent system, which integrates ANN and the Kalman filter, represents a notable advancement in wastewater management. It offers superior performance, accurate classification, and rapid response, making it a practical and effective solution for the management of water resources.