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Applying image recording and identification for measuring water stages to prevent flood hazards

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  • Han-Chung Yang
  • Chuan-Yi Wang
  • Jia-Xue Yang

Abstract

Information on river stages can be transmitted to relevant management offices over a network by using an automatic stage gauge so that management offices can effectively evaluate whether a river stage is exceeding the warning line and take action if necessary. However, current river stage measurement methods can present this information only in data form because the methods cannot simultaneously obtain images such as the rising or overflow of the river stage. In addition, the stage gauge can fail or be washed away when the river stage is high. To solve these problems, this research evaluates a novel measurement method involving a video surveillance system that exhibits features such as easy installation, low maintenance cost, and low failure possibility. Through on-site image recording, this measurement method involves using image identification technology to read water level figures automatically. This method offers instant river stage figures and on-site video so that disaster prevention measures can be implemented accordingly. The results of a dynamic water flow test conducted in an indoor experimental channel indicated that all of the average absolute error levels of river stage identification were less than ±1.2 %, meaning that the image identification technology could achieve identification results at any flow level. By contrast, the findings of a rainfall simulation experiment suggest that the average absolute error of river stage identification was less than ±2.5 %, meaning that the measurement technology and method used in this research are useful and feasible at various rainfall intensities. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Han-Chung Yang & Chuan-Yi Wang & Jia-Xue Yang, 2014. "Applying image recording and identification for measuring water stages to prevent flood hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 737-754, November.
  • Handle: RePEc:spr:nathaz:v:74:y:2014:i:2:p:737-754
    DOI: 10.1007/s11069-014-1208-2
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    References listed on IDEAS

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    1. Ulderica Parodi & Luca Ferraris, 2004. "Influence of Stage Discharge Relationship on the Annual Maximum Discharge Statistics," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 31(3), pages 603-611, March.
    2. Tew-Fik Mahdi, 2009. "Semi-two-dimensional numerical model for river morphological change prediction: theory and concepts," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(3), pages 565-603, June.
    3. Bruno Merz & Annegret Thieken, 2009. "Flood risk curves and uncertainty bounds," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 51(3), pages 437-458, December.
    4. Rothman, Adam J. & Levina, Elizaveta & Zhu, Ji, 2009. "Generalized Thresholding of Large Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 177-186.
    5. Gheorghe Romanescu & Ioan Nistor, 2011. "The effects of the July 2005 catastrophic inundations in the Siret River’s Lower Watershed, Romania," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 57(2), pages 345-368, May.
    6. R. Abbasov & R. Mahmudov, 2009. "Analysis of non climatic origins of floods in the downstream part of the Kura River, Azerbaijan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(2), pages 235-248, August.
    7. Heidi Kreibich & Annegret Thieken, 2009. "Coping with floods in the city of Dresden, Germany," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 51(3), pages 423-436, December.
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    Cited by:

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