Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 22, issue 12
Articles | Volume 22, issue 12
https://doi.org/10.5194/nhess-22-4139-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-4139-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 12
Research article
 | 
22 Dec 2022
Research article |  | 22 Dec 2022

A multi-strategy-mode waterlogging-prediction framework for urban flood depth

Zongjia Zhang, Jun Liang, Yujue Zhou, Zhejun Huang, Jie Jiang, Junguo Liu, and Lili Yang

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Abedin, S. and Stephen, H.: GIS Framework for Spatiotemporal Mapping of Urban Flooding, Geosci. J., 9, 77, https://doi.org/10.3390/geosciences9020077, 2019. 
Ali, M., Prasad, R., Xiang, Y., and Yaseen, Z. M.: Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts, J. Hydrol., 584, 124647, https://doi.org/10.1016/j.jhydrol.2020.124647, 2020. 
Ben Taieb, S., Bontempi, G., Atiya, A. F., and Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition, Expert Syst. Appl., 39, 7067–7083, https://doi.org/10.1016/j.eswa.2012.01.039, 2012. 
Chang, F., Chen, P., Lu, Y., Huang, E., and Chang, K.: Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control, J. Hydrol., 517, 836–846, https://doi.org/10.1016/j.jhydrol.2014.06.013, 2014. 
Danso-Amoako, E., Scholz, M., Kalimeris, N., Yang, Q., and Shao, J.: Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area, Comput. Environ. Urban, 36, 423–433, https://doi.org/10.1016/j.compenvurbsys.2012.02.003, 2012. 
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An innovative multi-strategy-mode waterlogging-prediction framework for predicting waterlogging depth is proposed in the paper. The framework selects eight regression algorithms for comparison and tests the prediction accuracy and robustness of the model under different prediction strategies. Ultimately, the accuracy of predicting water depth after 30 min can exceed 86.1 %. This can aid decision-making in terms of issuing early warning information and determining emergency responses in advance.
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