Wei Ming, She Lin, You Xue-Yi
Tianjin Engineering Center of Urban River Eco-purification Technology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China E-mail:
Water Sci Technol. 2020 Nov;82(9):1921-1931. doi: 10.2166/wst.2020.477.
The optimal layout of low-impact development (LID) facilities satisfying annual runoff control for low rainfall expectation is not effective under extreme rainfall conditions and urban waterlogging may occur. In order to avoid the losses of urban waterlogging, it is particularly significant to establish a waterlogging early warning system. In this study, based on coupling RBF-NARX neural networks, we establish an early warning system that can predict the whole rainfall process according to the rainfall curve of the first 20 minutes. Using the predicted rainfall process curve as rainfall input to the rainfall-runoff calculation engine, the area at risk of waterlogging can be located. The results indicate that the coupled neural networks perform well in the prediction of the hypothetical verification rainfall process. Under the studied extreme rainfall conditions, the location of 25 flooding areas and flooding duration are well predicted by the early warning system. The maximum of average flooding depth and flooding duration is 16.5 cm and 99 minutes, respectively. By predicting the risk area and the corresponding flooding time, the early warning system is quite effective in avoiding and reducing the losses from waterlogging.
满足低降雨预期下年径流控制的低影响开发（LID）设施的最优布局在极端降雨条件下并不有效，可能会发生城市内涝。为避免城市内涝造成的损失，建立内涝预警系统尤为重要。在本研究中，基于耦合径向基函数-非线性自回归外生（RBF-NARX）神经网络，我们建立了一个能够根据前20分钟的降雨曲线预测整个降雨过程的预警系统。将预测的降雨过程曲线作为降雨输入到降雨径流计算引擎中，可以定位内涝风险区域。结果表明，耦合神经网络在假设验证降雨过程的预测中表现良好。在研究的极端降雨条件下，预警系统对25个淹没区域的位置和淹没持续时间预测良好。平均淹没深度和淹没持续时间的最大值分别为16.5厘米和99分钟。通过预测风险区域和相应的淹没时间，预警系统在避免和减少内涝损失方面相当有效。
