Pakistan is among the most climate-vulnerable countries, facing recurring flash floods, glacial lake outburst floods (GLOFs), and hill torrents that endanger lives, livelihoods, and infrastructure. Advances in IoT technology has a promise for scalable flood detection sensors network in challenging terrains, yet their deployment in remote and underserved areas remains limited, leaving communities with little to no warning.
This project aimed to develop and deploy scalable IoT-based Flood Early Warning Systems (FEWS) for off-grid, high-risk regions. Initial field detections and technology validation were carried out in Swat and Namal, where upstream sensors successfully provided early warnings during flood events, helping refine design and deployment strategies.
The first operational deployments took place in Sindh, in partnership with the Aga Khan Agency for Habitat (AKAH), at five high-risk sites. These used a combination of LoRaWAN and GSM-based technologies to validate various flood scenarios and test long-range telemetry (up to 15 km from the nearest GSM signal). The deployments covered canal overflows and direct riverine flooding, enabling real-time alerts in communities previously beyond the reach of traditional networks.
Our system combines modular, solar-powered sensors, LoRa-Mesh devices, and custom-built GSM nodes, ensuring reliable performance in off-grid environments. Emphasizing community integration, we collaborated closely with AKAH to align early warning with preparedness and shelter planning.
The project demonstrates a replicable model for low-cost, scalable flood warning infrastructure, contributing to greater community safety, data-informed disaster response, and local capacity for long-term sustainability.
