Depth prediction of urban waterlogging based on BiTCN-GRU modeling.
With China's rapid urbanization and the increasing frequency of extreme weather events, heavy rainfall-induced urban waterlogging has become a persistent and pressing challenge. Accurately predicting waterlogging depth is essential for disaster prevention and loss mitigation. However, existing...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0321637 |
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| Summary: | With China's rapid urbanization and the increasing frequency of extreme weather events, heavy rainfall-induced urban waterlogging has become a persistent and pressing challenge. Accurately predicting waterlogging depth is essential for disaster prevention and loss mitigation. However, existing hydrological models often require extensive data and have complex structures, resulting in low prediction accuracy and limited generalization capabilities. To address these challenges, this paper proposes a hybrid deep learning-based approach, the BiTCN-GRU model, for predicting waterlogging depth in urban flood-prone areas. This model integrates Bidirectional Temporal Convolutional Networks (BiTCN) and Gated Recurrent Units (GRU) to enhance prediction performance. Specifically, the gated recurrent units (GRU) is employed for this prediction task. Bidirectional temporal convolutional network (BiTCN) can effectively capture the information features during rainfall and waterlogging depth by forward and backward convolution and use them as inputs to GRU. Experimental results demonstrate the great performance of the proposed model, achieving MAE, RMSE, and R2 values of 1.56, 3.62, and 88.31% for Minshan Road, and 3.44, 8.08, and 92.64% for Huaihe Road datasets, respectively. Compared to models such as GBDT, LSTM, and TCN-LSTM, the BiTCN-GRU model exhibits higher accuracy in predicting waterlogging depth. This hybrid model provides a robust solution for short-term waterlogging prediction, offering valuable scientific insights and theoretical support for urban waterlogging disaster prevention and mitigation. |
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| ISSN: | 1932-6203 |