Abstract
Sensitivity analysis of urban flood model parameters is important for urban flood simulation. Efficient and accurate acquisition of sensitive parameters is the key to real-time model calibration. In order to quickly obtain the sensitive runoff parameters of the urban flood simulation model, this study proposes an artificial neural network-based identification method for sensitive parameters. Artificial neural network (ANN) models were constructed with the binary classification and multi-classification methods, and used environmental indicators that affect the parameter sensitivity of different hydrological response units as the input, with the sensitivity parameters of the Storm water management model (SWMM) being the output. The optimization of the ANN was realized by adjusting the number of nodes in the hidden layer and the maximum number of iterations. An example application was conducted in Zhengzhou, China. The results show that the binary classification ANN quickly identified sensitive parameters, and the prediction accuracy of all parameters exceeded 96%. Convergence can be achieved when the number of nodes in the hidden layer does not exceed twice the number of input nodes, and the maximum number of iterations does not exceed 200. Rapid and accurate identification of the sensitive runoff parameters of the urban flood simulation model was achieved, which reduced the time required for parameter sensitivity analysis.
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Funding
This research was funded by the Key Program of National Natural Science Foundation of China (Grant No: 51739009) and the National Natural Science Foundation for Young Scientists of China (Grant No. 51909240). The authors thank the anonymous reviewers for their valuable comments.
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Ma, Methodology, Validation, Writing original draft, Visualization. Z Wu, Conceptualization, Project administration, Supervision. H Wang & H Lv, Writing original draft, Visualization. C Hu & X Zhang, Conceptualization, Writing review & editing, Funding acquisition. All authors have read and approved the final manuscript.
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Animal experiments were performed in accordance with protocol approved by the Service de la Consommation et des Affaires vtrinaires of Canton de Vaud (VD 1541.4 and VD 1865.3). NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ mice (NSG) breeders were purchased from Jackson Laboratories
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Wu, Z., Ma, B., Wang, H. et al. Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network. Water Resour Manage 35, 2115–2128 (2021). https://doi.org/10.1007/s11269-021-02825-3
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DOI: https://doi.org/10.1007/s11269-021-02825-3