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Real-time Extreme Rainfall Evaluation System for the Construction Industry Using Deep Convolutional Neural Networks

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  • Chih-Chiang Wei

    (National Taiwan Ocean University)

Abstract

For the construction industry, timely and reliable information on current and future rain information is vital for enabling forecasters to make accurate and timely forecasts and to allow appropriate construction operations. Because construction works are often delayed during typhoons, a useful scheme for rain forecasts during typhoon periods is highly desirable. This study developed a regional extreme precipitation and construction suspension estimation system (REPCSES) for the construction industry to use when a structure is in the construction stage. The REPCSES has two major functions: a regional extreme precipitation estimation model (comprising Modules 1 and 2) and the construction suspension estimation model (Modules 3 and 4). Module 1 is a regional 1-h-ahead rainfall estimation model, which is used for estimating the hourly rainfall near the construction location. Module 2 is used for estimating the cumulative rainfall within 24 h. Module 3 is designed to plot a hyetograph using the results from Modules 1 and 2. Then, Module 4 determines whether the construction should be suspended according to the plots from Module 3. In addition, this study developed a deep convolutional neural network model for estimating extreme rainfall during a structure under construction, and the experimental area was Nantou County, Taiwan. The collected typhoons (i.e., Soulik, Trami, Kong-Rey, Matmo, Dujuan, and Nesat) affecting the study area occurred from 2013 to 2017. The results indicated that the integrated system could provide accurate estimations of whether work could proceed as well as the number of days that construction should be suspended for.

Suggested Citation

  • Chih-Chiang Wei, 2020. "Real-time Extreme Rainfall Evaluation System for the Construction Industry Using Deep Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2787-2805, July.
  • Handle: RePEc:spr:waterr:v:34:y:2020:i:9:d:10.1007_s11269-020-02580-x
    DOI: 10.1007/s11269-020-02580-x
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    References listed on IDEAS

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    1. Cheng-Shang Lee & Li-Rung Huang & Horng-Syi Shen & Shi-Ting Wang, 2006. "A Climatology Model for Forecasting Typhoon Rainfall in Taiwan," 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. 37(1), pages 87-105, February.
    2. Jhih-Huang Wang & Gwo-Fong Lin & Ming-Jui Chang & I-Hang Huang & Yu-Ren Chen, 2019. "Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3759-3780, September.
    3. Duo Zhang & Nicolas Martinez & Geir Lindholm & Harsha Ratnaweera, 2018. "Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(6), pages 2079-2098, April.
    4. Jairo Diaz-Ramirez & William McAnally & James Martin, 2012. "Sensitivity of Simulating Hydrologic Processes to Gauge and Radar Rainfall Data in Subtropical Coastal Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3515-3538, September.
    5. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
    6. Dong-Sin Shih & Ming-Hsu Li & Ray-Shyan Wu, 2008. "Distributed Flood Simulations with Coupling Gauge Observations and Radar-rainfall Estimates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(7), pages 843-859, July.
    7. Neamat Karimi & Mohammad Hossein Bagheri & Farhad Hooshyaripor & Ashkan Farokhnia & Sara Sheshangosht, 2016. "Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5003-5020, November.
    8. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
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