IOT-Enhanced Wastewater Management Using CRNN Towards Sustainable Smart Cities

Abstract

Traditional methods of wastewater treatment have been stretched to their limits because of the exponential expansion in wastewater production that has been caused by urbanisation. It is necessary for smart cities to implement solutions that are not just effective but also technologically advanced if they are to achieve ecological sustainability. The ability to monitor and adjust in real time is typically absent from the wastewater management systems that are currently in utilisation. According to the findings of the research, there is a gap in the combination of the Internet of Things and Convolutional Recurrent Neural Networks (CRNN), which might lead to improved management strategies. This is necessary to address the ever-changing composition and movement of wastewater. According to the findings of the study, a novel approach might be implemented by utilising CRNN for predictive analytics and Internet of Things (IoT) sensors for real-time data collection methods. Together, they make it feasible to predict problems and maintain a close eye on the characteristics of wastewater in real time, which paves the way for proactive management techniques. It is because of the results, which reveal a significant improvement in the accuracy of predicting important wastewater parameters, that timely actions are now possible. The IoT and CRNN have been combined to provide enhanced adaptation to changing conditions, which has led to wastewater treatment techniques that are more sustainable and efficient.