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  4. Wireless sensor network for AI-based flood disaster detection
 
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2022
Journal Article
Title

Wireless sensor network for AI-based flood disaster detection

Abstract
In recent decades, floods have led to massive destruction of human life and material. Time is of the essence for evacuation, which in turn is determined by early warning systems. This study proposes a wireless sensor network decision model for the detection of flood disasters by observing changes in weather conditions compared to historical information at a given location. To this end, we collected data such as air pressure, wind speed, water level, temperature and humidity (DH11), and precipitation (0/1) from sensors located at several points in the area under consideration and obtained sea level air pressure and rainfall from the Google API. The collected data was then transmitted via a LoRaWAN network implemented in Raspberry-Pi and Arduino. The developed support vector machine (SVM) model includes a number of coordinators responsible for a number of sectors (locations). The SVM model sends the binary decisions (flood or no flood) with an accuracy of 98% to a cloud server connected to monitoring rooms, where a decision can be made regarding the response to a possible flood disaster.
Author(s)
Al Qundus, Jamal
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Dabbour, Kosai
EVA Electronics Co., Kuwait
Gupta, Shivam
NEOMA Business School, Reims, France
Meissonier, Régis
University of Montpellier, France
Paschke, Adrian  
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Journal
Annals of operations research  
DOI
10.1007/s10479-020-03754-x
Language
English
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Keyword(s)
  • disaster detection

  • wireless sensor network

  • artificial intelligence

  • support vector machine

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