Advancing Sensor Network Optimization and Data Analytics for Smart Wastewater and Stormwater Collection Systems: An Overview

As the foundation of monitoring systems, knowledge of the stormwater and wastewater collection systems' performance is essential to preserving structural integrity, averting system failure, and guaranteeing their dependable and effective daily operation under typical circumstances. Nevertheless, these subterranean systems encounter major obstacles because of restricted access, occasional examinations, and aging-related deterioration, such as corrosion, obstruction, and fouling, in addition to high demands brought on by population growth and the constant rise in stormwater demand brought on by flooding. Real-time monitoring, predictive maintenance, and consequently prompt decision-making are made possible by recent developments in sensing, artificial intelligence (AI), machine learning (ML), and decision support systems. In keeping with this, this paper provides a summary of the most recent developments in sensor network optimization by incorporating AI-driven data analytics and decision tools to enhance operational efficiency, infrastructure resilience, and data processing. To discover how different optimization techniques, such as heuristics, metaheuristics, and reinforcement learning, can improve sensor placement strategies and strike a compromise between cost limitations and data observability, they are compiled here. The advantages of AI-based predictive analytics in enhancing wastewater monitoring, cutting maintenance expenses, and enhancing regulatory compliance are illustrated by case examples.