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A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network

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  • Lin She

    (Tianjin University)

  • Xue-yi You

    (Tianjin University)

Abstract

Dynamic flow forecast, which is one of the critical technologies in the field of future Intelligent Drainage, has great potential for mitigating the damages resulting from extreme rainfalls. This study aims to develop a coupled neural network called RBF-NARX Forecast Model (RNFM) to predict urban drainage outflow. RNFM integrates the architecture advantages of the radial basis function neural network (RBFNN) and the nonlinear autoregressive with an exogenous inputs neural network (NARXNN). By calculating the Square Sum of Error (SSE) between RNFM predictions and SWMM simulations, the network parameters are optimized and the optimal coupling site of RBFNN and NARXNN is found. The urban drainage in Tianjin is presented to justify the feasibility of RNFM, and the average SSE in test rainfalls is only 0.273. Based on the Monte Carlo simulations (MCS), the uncertainty analysis is quantified and the SWMM simulations lie within the 95% prediction confidential interval, which proves that RNFM have great potential in predictions and management of urban runoff.

Suggested Citation

  • Lin She & Xue-yi You, 2019. "A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3143-3153, July.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02294-9
    DOI: 10.1007/s11269-019-02294-9
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    References listed on IDEAS

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    1. Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    2. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
    3. Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
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    Cited by:

    1. Hongfa Wang & Xinjian Guan & Yu Meng & Zening Wu & Kun Wang & Huiliang Wang, 2023. "Coupling Time and Non-Time Series Models to Simulate the Flood Depth at Urban Flooded Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1275-1295, February.
    2. Zening Wu & Bingyan Ma & Huiliang Wang & Caihong Hu & Hong Lv & Xiangyang Zhang, 2021. "Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2115-2128, May.

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