Deep learning models have become increasingly popular for flood prediction
due to their superior accuracy and efficiency compared to traditional methods.
However, current machine learning methods often rely on separate spatial or
temporal feature analysis and have limitations on the types, number, and
dimensions of input data. This study presented a CNN-RNN hybrid feature fusion
modelling approach for urban flood prediction, which integrated the strengths
of CNNs in processing spatial features and RNNs in analyzing different
dimensions of time sequences. This approach allowed for both static and dynamic
flood predictions. Bayesian optimization was applied to identify the seven most
influential flood-driven factors and determine the best combination strategy.
By combining four CNNs (FCN, UNet, SegNet, DeepLabv3+) and three RNNs (LSTM,
BiLSTM, GRU), the optimal hybrid model was identified as LSTM-DeepLabv3+. This
model achieved the highest prediction accuracy (MAE, RMSE, NSE, and KGE were
0.007, 0.025, 0.973 and 0.755, respectively) under various rainfall input
conditions. Additionally, the processing speed was significantly improved, with
an inference time of 1.158s (approximately 1/125 of the traditional computation
time) compared to the physically-based models.

本研究提出了基于 CNN-RNN 混合特征融合建模的城市洪水预测方法。通过结合卷积神经网络在处理空间特征方面的优势和循环神经网络在分析不同时间序列维度方面的优势，实现了静态和动态洪水预测。通过贝叶斯优化，最佳的混合模型被确定为 LSTM-DeepLabv3+，在各种雨量输入条件下都取得了最高的预测精度。该模型的推理时间大大提高，相对于基于物理的模型的传统计算时间约为 1 / 125。