Optimizing Sewage Systems for Real-Time Flow Analysis and Predictive Maintenance with IoT and LSTM

This research introduces a new real-time method for analyzing sewage flow utilizing the Internet of Things (IoT). This method relies on long-short-term memory (LSTM) networks. In urban areas, sewage systems are an integral part of the infrastructure, and keeping them in good working order is crucial to reducing pollution and protecting residents’ health. System breakdowns and expensive repairs are common results of the reactive and inefficient maintenance practices commonly used in the past. In this research, it presents preventative methods that make use of IoT sensors to gather real-time data from wastewater treatment facilities. The information is then analyzed using LSTM networks to foresee problems like backups and overflows. Municipalities and utility companies may save money and lessen their environmental impact by implementing preventative maintenance plans that address potential issues in advance. It examines the practical implications of applying IoT and LSTM-based predictive maintenance to sewage systems and gives experimental findings confirming the technique’s usefulness in properly predicting flow irregularities. This study is crucial to ensuring the long-term viability and effective operation of urban sewage systems.