Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management.

高效，准确的流量预测对城市水管理至关重要。数据驱动模型，尤其是神经网络（NN）模型可以快速预测流量，而在某些复杂的河流系统中，结果不确定。基于物理的模型可以揭示潜在的物理原理，但是它相对较慢且计算成本很高。这项工作着眼于在以下方面评估三种NN模型（人工神经网络（ANN），长期短期记忆网络（LSTM），自适应神经模糊推理系统（ANFIS））和一种基于物理的模型（SOBEK）的可靠性。平均和峰值流量模拟的效率和准确性。所有模型都适用于深圳的潮河和山区河流。结果表明，由于隐层的结构简单，因此人工神经网络模型的计算速度最快。LSTM模型在潮汐河的平均流量模拟中具有最低的偏差，因此是可靠的，而ANFIS模型在峰值流量模拟中具有最高的精度。此外，SOBEK模型具有捕获山区不均匀空间降雨的能力，因此在模拟山区河流的平均流量和峰值流量方面显示出可靠性。总体而言，结果表明，通过给出通常可接受的准确度的快速流量预测，LSTM模型可以为复杂城市河流系统流量模拟中基于物理的模型提供有用的补充。我们的结果可以为水文工程师在洪水风险管理的背景下洪水预警和应急准备中的应用提供有用的信息。