Emergency navigation and alarm with flooding models — A real case study of Manado City

Abstract

This work proposes a framework to provide emergency navigation for people within flooding events. Real-time data from weather stations are downloaded for the flooding simulation in our framework. Sensors are disposed along the river, and the collected sensor data can be used to update our simulation results to provide more accurate estimation of flooding events. After flooding models are constructed, this work further includes and modifies the Dijkstra algorithm for the navigation. Our navigation algorithm dynamically considers the traveling speed of users, the real-time flooding data, and the estimation of flooding to provide the best route which is not only the shortest path to destinations but also relatively safe. In our prototype implementation, the map of Manado City is included in our experiments, and the traces of the real flooding data of Manado City are also included as the input of our experiments. The experimental results show that our framework can immediately provide alarm and navigation when a node in the map is prone to be flooded, and the provided routes can avoid flooding nodes in the map.