In the context of global climate change and the continuous development of urban areas, rainfall-inundation modeling is a common approach that provides critical support for the protection and early warning of urban waterlogging protection. The present study conducts a data-driven model for hourly urban rainfall-inundation depth prediction, which is based on a gated recurrent unit (GRU) neural network and uses the simulated annealing (SA) algorithm for the hyperparameter optimization of GRU, namely the SA-GRU model. To verify the performance of the proposed model, backpropagation, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) neural networks are set as benchmarks. Results show that the SA-GRU has high accuracy in the case of short-term inundation prediction, with the Nash–Sutcliffe efficiency from 0.999 to 0.596 for the 1-h-ahead to 8-h-ahead predictions. And further research reveals that the SA-GRU integrates the significant optimization of SA, with an average 20% reduction of the root mean square error within the first eight prediction periods, and the efficient training speed of GRU, with 23.7% faster than LSTM and 44.2% faster than BiLSTM. In conclusion, the SA-GRU excels in urban inundation prediction, demonstrating its value in flood management and decision-making.
A GRU-based neural network was used for urban waterlogging modeling to make inundation depth predictions for 1–12 h.
A simulated annealing (SA) algorithm was used as the hyperparameter optimization method to boost the performance of GRU.
The SA-GRU model has better performance rather than SA-BP, SA-LSTM, and SA-BiLSTM within its prediction period threshold (8 h).