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

  • Zhanya Xu
  • Xiangang Luo
  • Shuang Zhu
  • Di Wu
  • Qi Guo
Chapter
  • 28 Downloads

Abstract

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.

Keywords

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

References

  1. 1.
    E. Gaume, V. Bain, P. Bernardara, O. Newinger, M. Barbuc, A. Bateman, L. Blaškovičová, G. Blöschl, M. Borga, A. Dumitrescu, I. Daliakopoulos, J. Garcia, A. Irimescu, S. Kohnova, A. Koutroulis, L. Marchi, S. Matreata, V. Medina, E. Preciso, D. Sempere-Torres, G. Stancalie, J. Szolgay, I. Tsanis, D. Velasco, A. Viglione, A compilation of data on European flash floods. J. Hydrol. 367(1-2), 70–78 (2009)CrossRefGoogle Scholar
  2. 2.
    S.N. Jonkman, Global perspectives on loss of human life caused by floods. Nat. Hazards 34(2), 151–175 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    D. Liu, S. Zhong, Q. Huang, Study on risk assessment framework for snowmelt flood and hydro-network extraction from watersheds, in Geo-Informatics in Resource Management and Sustainable Ecosystem (Springer, Berlin, 2015), pp. 638–651Google Scholar
  4. 4.
    M. Borga, E. Gaume, J.D. Creutin, L. Marchi, Surveying flash floods: gauging the ungauged extremes. Hydrol. Process. 22(18), 3883–3885 (2008)CrossRefGoogle Scholar
  5. 5.
    M.S. Chubey, S. Hathout, Integration of RADARSAT and GIS modelling for estimating future Red River flood risk. GeoJournal 59(3), 237–246 (2004)CrossRefGoogle Scholar
  6. 6.
    A. Bröring, J. Echterhoff, S. Jirka, I. Simonis, T. Everding, C. Stasch, S. Liang, R. Lemmens, New generation sensor web enablement. Sensors 11(3), 2652–2699 (2011)CrossRefGoogle Scholar
  7. 7.
    F. Zhang, S. Zhong, S. Yao, C. Wang, Q. Huang, Ontology-based representation of meteorological disaster system and its application in emergency management: illustration with a simulation case study of comprehensive risk assessment. Kybernetes 45(5), 798–814 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    S. Zhong, Z. Fang, M. Zhu, Q. Huang, A geo-ontology-based approach to decision-making in emergency management of meteorological disasters. Nat. Hazards 89(2), 531–554 (2017)CrossRefGoogle Scholar
  9. 9.
    R. Mariappan, P.V. Narayana Reddy, C. Wu, Cyber physical system using intelligent wireless sensor actuator networks for disaster recovery, in 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (2015), pp. 95–99Google Scholar
  10. 10.
    R. (Raj) Rajkumar, I. Lee, L. Sha, J. Stankovic, Cyber-physical systems, in Proceedings of the 47th Design Automation Conference on—DAC’10 (2010), p. 731Google Scholar
  11. 11.
    C.I. Wu, H.Y. Kung, C.H. Chen, L.C. Kuo, An intelligent slope disaster prediction and monitoring system based on WSN and ANP. Expert Syst. Appl. 41(10), 4554–4562 (2014)CrossRefGoogle Scholar
  12. 12.
    C.W. Callaghan, Disaster management, crowdsourced R&D and probabilistic innovation theory: toward real time disaster response capability. Int. J. Disaster Risk Reduct. 17, 238–250 (2016)CrossRefGoogle Scholar
  13. 13.
    A. Mosenia, S. Sur-Kolay, A. Raghunathan, N.K. Jha, DISASTER: dedicated intelligent security attacks on sensor-triggered emergency responses. IEEE Trans. Multi-Scale Comput. Syst. 3(4), 255–268 (2017)CrossRefGoogle Scholar
  14. 14.
    J. Wan, D. Zhang, S. Zhao, L. Yang, J. Lloret, Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Commun. Mag. 52(8), 106–113 (2014)CrossRefGoogle Scholar
  15. 15.
    C. Xiao, N. Chen, J. Gong, W. Wang, C. Hu, Z. Chen, Event-driven distributed information resource-focusing service for emergency response in smart city with cyber-physical infrastructures. ISPRS Int. J. Geo Inf. 6(8), 251 (2017)Google Scholar
  16. 16.
    I. Kureshi, G. Theodoropoulos, E. Mangina, G. O’Hare, J. Roche, Towards an info-symbiotic decision support system for disaster risk management, in 2015 IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (2015), pp. 85–91Google Scholar
  17. 17.
    C.O. Rolim, F.L. Koch, C.B. Westphall, J. Werner, A. Fracalossi, G.S. Salvador, A cloud computing solution for patient’s data collection in health care institutions, in 2nd International Conference on eHealth, Telemedicine, and Social Medicine, eTELEMED 2010, Includes MLMB 2010; BUSMMed 2010, vol. 12(ii) (2010), pp. 95–99Google Scholar
  18. 18.
    M. Di, M.J. Er, A survey of machine learning in wireless sensor networks—from networking and application perspectives, in 2007 6th International Conference on Information, Communications and Signal Processing (ICICS) (2007), pp. 1–5Google Scholar
  19. 19.
    A. Förster, A. Murphy, Machine learning across the WSN layers, in Cdn.Intechweb.Org (2004), pp. 165–183Google Scholar
  20. 20.
    R.V. Kulkarni, A. Förster, G.K. Venayagamoorthy, Computational intelligence in wireless sensor networks: a survey. IEEE Commun. Surv. Tutorials 13(1), 68–96 (2011)CrossRefGoogle Scholar
  21. 21.
    R.W. Skowyra, A. Lapets, A. Bestavros, A. Kfoury, Verifiably-safe software-defined networks for CPS, in Proceedings of the 2nd ACM International Conference on High Confidence Networked Systems (HiCoNS ’13) (ACM, New York, 2013), p. 101CrossRefGoogle Scholar
  22. 22.
    S. Zhu, J. Zhou, L. Ye, et al. Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, Environ. Ear. Sci. China [J], 2016, 75(6), p. 531Google Scholar
  23. 23.
    J. Langhammer, J. Česák, Applicability of a nu-support vector regression model for the completion of missing data in hydrological time series. Water (Switzerland) 8(12), 560 (2016)Google Scholar
  24. 24.
    A.J. Rettig, S. Khanna, D. Heintzelman, R.A. Beck, An open source software approach to geospatial sensor network standardization for urban runoff. Comput. Environ. Urban. Syst. 48, 28–34 (2014)CrossRefGoogle Scholar
  25. 25.
    P. Coulibaly, N.D. Evora, Comparison of neural network methods for infilling missing daily weather records. J. Hydrol. 341(1-2), 27–41 (2007)CrossRefGoogle Scholar
  26. 26.
    A. Elshorbagy, S.P. Simonovic, U.S. Panu, Estimation of missing streamflow data using principles of chaos theory. J. Hydrol. 255(1-4), 123–133 (2002)CrossRefGoogle Scholar
  27. 27.
    A.J. Abebe, D.P. Solomatine, R.G.W. Venneker, Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events. Hydrol. Sci. J. 45(3), 425–436 (2000)CrossRefGoogle Scholar
  28. 28.
    A.K. Srivastava, M. Rajeevan, S.R. Kshirsagar, Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos. Sci. Lett. 10(October), 249–254 (2009)Google Scholar
  29. 29.
    M.P. Tingley, P. Huybers, A Bayesian algorithm for reconstructing climate anomalies in space and time. Part I: Development and applications to paleoclimate reconstruction problems. J. Climate 23(10), 2759–2781 (2010)Google Scholar
  30. 30.
    A. Bárdossy, G. Pegram, Infilling missing precipitation records—a comparison of a new copula-based method with other techniques. J. Hydrol. 519(PA), 1162–1170 (2014)Google Scholar
  31. 31.
    M.T. Dastorani, A. Moghadamnia, J. Piri, M. Rico-Ramirez, Application of ANN and ANFIS models for reconstructing missing flow data. Environ. Monit. Assess. 166(1–4), 421–434 (2010)CrossRefGoogle Scholar
  32. 32.
    S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar

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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