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Deep learning models have become increasingly popular for flood predictiondue to their superior accuracy and efficiency compared to traditional methods.However, current machine learning methods often rely on separate spatial ortemporal feature analysis and have limitations on the types, number, anddimensions of input data. This study presented a CNN-RNN hybrid feature fusionmodelling approach for urban flood prediction, which integrated the strengthsof CNNs in processing spatial features and RNNs in analyzing differentdimensions of time sequences. This approach allowed for both static and dynamicflood predictions. Bayesian optimization was applied to identify the seven mostinfluential 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+. Thismodel achieved the highest prediction accuracy (MAE, RMSE, NSE, and KGE were0.007, 0.025, 0.973 and 0.755, respectively) under various rainfall inputconditions. Additionally, the processing speed was significantly improved, withan inference time of 1.158s (approximately 1/125 of the traditional computationtime) compared to the physically-based models.

Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion