EMoS Published: Floodwater detection

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EMoS Published: Floodwater detection

Flood detection

EMoS student Ange Josiane Uwayisenga has made a notable contribution by publishing her work on IoT-based system for automated floodwater detection and early warning in the EAC region; a case study of Arusha and Dar es Salaam, Tanzania. The paper was supervised by Dr. Mussa Ally and Dr. Neema Mduma.

Although several countries have been affected by natural disasters, this work focuses on floods as the most frequent disaster in the East African region.

The study aims at developing a low-cost system that detects and alerts the community on upcoming flood incidents. The proposed floodwater detection and early warning system comprise of three units.

The sensing unit continuously monitors environmental parameters using ultrasonic, temperature and humidity sensors. The processing unit processes and analyses the collected data from the sensors. Then, the alerting unit alerts the community and local authorities using a buzzer and a Short Message Service (SMS) notification.

The system uses the global system for mobile communication to provide internet connectivity which enables data to be collected, stored, and monitored in the cloud. The system was implemented at Themi river as one of the case study areas.