Machine Learning Algorithms for Real-Time Monitoring of Wastewater Treatment Efficiency

Combining the machine learning techniques into real-time monitoring systems greatly improves the operation of wastewater treatment plants. Conventional methods sample manually (not in real time), and analysis is time-dominative, so there is no time for intervention or optimization. This work mitigates these limitations by using supervised and unsupervised machine learning techniques for real-time sensor data processing, treatment prediction, and anomaly detection. Based on techniques like random forest for predicting, and k-means for finding anomaly, the system reached a detection accuracy of around 92%, and time spent on corrective action was reduced by 30% compared to manual approaches. The superior performance of sensor data treatment was validated by key performance metrics such as 0.025 mean squared error for random forest predictions. Not only does it achieve better monitoring and control, but it also reduces the risk of environmental non-compliance thus leading toward the practice of sustainable water resources. The significance of this work the in the platform's ability to illustrate how real-time, data-driven via machine learning technologies can enable more responsive and responsive waste treatment systems and provides a foundation for future environmental analytics platforms.