A CPS-Improved Data Estimation Model for Flash Flood Early Warning Sensor Network

  • Zhanya Xu
  • Xiangang Luo
  • Shuang Zhu
  • Di Wu
  • Qi Guo


In recent decades, to provide accurate and reliable warning of impending flash flood disasters, wireless sensor networks consisting of numerous hydrological and meteorological physical sensors have been widely used. However, due to the harsh natural environment and hazardous aftermath of flooding disasters, the equipment damage and data anomalies caused by physical failures and the working environment directly affect the reliability of early warning systems. To monitor all types of anomalies in real-time and to provide reliable data services, it is necessary to design appropriate models for early warning systems composed of physical devices, networks, and computing facilities (Cyber Physical System) to improve their adaptability to disaster environments. This report presents a data service framework for a flash flood warning sensor network and proposes a data estimation model based on a long short-term memory network for real-time processing and service of data anomalies. The model is trained using historical hydrological data and can establish data association relationships for the sensors under different meteorological conditions. In addition, it can provide reliable data services when abnormal data is detected. Based on experiments in actual scenarios, different sites in the study area can generate high-precision data estimates under different meteorological conditions, which can simplify the data analysis process and effectively apply the data service of the flash flood warning sensor network.


Long short-term memory Cyber physical system Flash flood early warning 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhanya Xu
    • 1
    • 2
  • Xiangang Luo
    • 1
    • 2
  • Shuang Zhu
    • 1
    • 2
  • Di Wu
    • 1
    • 2
  • Qi Guo
    • 3
  1. 1.School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China
  2. 2.National Engineering Research Center for Geographic Information SystemWuhanChina
  3. 3.Wuhan Tianhong Lightning Protection Testing Center Development Co., Ltd.WuhanChina

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