An Approach for IoT-Based Smart Sensors Placement in Urban Water Networks Under Natural Hazards

Natural hazards such as floods, storms, and earthquakes present significant threats to urban infrastructures, particularly water supply and distribution networks. These events can severely impact the quality and quantity of water resources, leading to serious consequences for public health and social security. Factors such as unplanned urban development and non-compliance with engineering standards further increase the vulnerability of these systems. Recent advancements in technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) have enabled real-time monitoring and data analysis of these critical infrastructures. IoT-based smart sensors capture essential information, including flow rate, water quality, corrosion, leakage, and pipeline ruptures. These data are processed using machine learning and deep learning algorithms to identify anomalies. Such systems can enhance monitoring capabilities and support effective decision-making in crisis situations. This study explores key criteria for selecting optimal locations for sensor deployment. These criteria include connection points, infrastructure accessibility, water quality, natural hazard risks, and historical incident data. For example, evaluating the location of connection points and their impact on water flow and distribution can help identify optimal routes, reducing costs and response times. Easy access to infrastructure facilitates sensor installation and maintenance, improving system efficiency. Monitoring water quality at various points in the distribution network is also critical to identifying sensitive locations and ensuring water safety. Additionally, identifying areas prone to natural hazards helps prioritize vulnerable regions for monitoring and improve system resilience. Historical data on anomalies and past incidents provide patterns that highlight risk-prone areas and help refine monitoring strategies. Based on these criteria, a multi-criteria decision-making approach is applied to propose the most effective locations for sensor placement. This method suggests prioritizing locations that have the highest impact and accessibility. These recommendations aim to enhance system efficiency and improve response capabilities during emergencies.

Ketwords: Smart Infrastructures, Internet of Things, MCDM, Artificial Intelligence, Natural Hazards