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A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network

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  • Jing Huang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
    Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

  • Jinle Kang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
    Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

  • Huimin Wang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
    Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

  • Zhiqiang Wang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
    Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

  • Tian Qiu

    (Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

Abstract

Quickly obtaining accurate waterlogging depth data is vital in urban flood events, especially for emergency response and risk mitigation. In this study, a novel approach to measure urban waterlogging depth was developed using images from social networks and traffic surveillance video systems. The Mask region-based convolutional neural network (Mask R-CNN) model was used to detect tires in waterlogging, which were considered to be reference objects. Then, waterlogging depth was calculated using the height differences method and Pythagorean theorem. The results show that tires detected from images can been used as an effective reference object to calculate waterlogging depth. The Pythagorean theorem method performs better on images from social networks, and the height differences method performs well both on the images from social networks and on traffic surveillance video systems. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

Suggested Citation

  • Jing Huang & Jinle Kang & Huimin Wang & Zhiqiang Wang & Tian Qiu, 2020. "A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:2149-:d:330914
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    References listed on IDEAS

    as
    1. Yang Xiao & Beiqun Li & Zaiwu Gong, 2018. "Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data," 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. 94(2), pages 833-842, November.
    2. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    3. repec:kap:iaecre:v:15:y:2009:i:4:p:409-420 is not listed on IDEAS
    4. Louie Ren & Yong Glasure, 2009. "Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(4), pages 409-420, November.
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