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.
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.
the Creative Commons Attribution 4.0 License.
A multi-strategy-mode waterlogging-prediction framework for urban flood depth
Zongjia Zhang
School of Environment, Harbin Institute of Technology, Harbin, 150001, China
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
Jun Liang
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
Yujue Zhou
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
Zhejun Huang
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
Jie Jiang
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
Junguo Liu
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
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Cited
13 citations as recorded by crossref.
- Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. 10.3390/rs15082024
- Effectiveness Evaluation of Emergency Rescuing Plans Oriented to Urban Waterlogging Based on a Neural Network Model H. Zhang et al. 10.1109/ACCESS.2024.3368864
- Urban rainstorm and waterlogging scenario simulation based on SWMM under changing environment S. Wang et al. 10.1007/s11356-023-31027-0
- Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification Z. Zhang et al. 10.3390/ijerph20032528
- Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. 10.3390/jmse11112154
- The spatial overlay effect of urban waterlogging risk and land use value Y. Ding et al. 10.1016/j.scitotenv.2024.174290
- Modeling and application of urban waterlogging resilience assessment based on PSR–NES framework and analysis of driving mechanisms Y. Gao et al. 10.2166/wst.2024.295
- Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters N. Ma et al. 10.3390/en17051165
- Monitoring and early warning mechanism of flood invasion into subway tunnels based on the experimental study of flooding patterns W. Dong et al. 10.26599/JIC.2024.9180011
- Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning J. Wang & N. Wang 10.1016/j.ress.2024.110452
- An intelligent framework for spatiotemporal simulation of flooding considering urban underlying surface characteristics H. Jin et al. 10.1016/j.jag.2024.103908
- Integration of an improved transformer with physical models for the spatiotemporal simulation of urban flooding depths H. Jin et al. 10.1016/j.ejrh.2023.101627
- Flood prediction with time series data mining: Systematic review D. Hakim et al. 10.1016/j.nhres.2023.10.001
13 citations as recorded by crossref.
- Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. 10.3390/rs15082024
- Effectiveness Evaluation of Emergency Rescuing Plans Oriented to Urban Waterlogging Based on a Neural Network Model H. Zhang et al. 10.1109/ACCESS.2024.3368864
- Urban rainstorm and waterlogging scenario simulation based on SWMM under changing environment S. Wang et al. 10.1007/s11356-023-31027-0
- Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification Z. Zhang et al. 10.3390/ijerph20032528
- Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. 10.3390/jmse11112154
- The spatial overlay effect of urban waterlogging risk and land use value Y. Ding et al. 10.1016/j.scitotenv.2024.174290
- Modeling and application of urban waterlogging resilience assessment based on PSR–NES framework and analysis of driving mechanisms Y. Gao et al. 10.2166/wst.2024.295
- Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters N. Ma et al. 10.3390/en17051165
- Monitoring and early warning mechanism of flood invasion into subway tunnels based on the experimental study of flooding patterns W. Dong et al. 10.26599/JIC.2024.9180011
- Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning J. Wang & N. Wang 10.1016/j.ress.2024.110452
- An intelligent framework for spatiotemporal simulation of flooding considering urban underlying surface characteristics H. Jin et al. 10.1016/j.jag.2024.103908
- Integration of an improved transformer with physical models for the spatiotemporal simulation of urban flooding depths H. Jin et al. 10.1016/j.ejrh.2023.101627
- Flood prediction with time series data mining: Systematic review D. Hakim et al. 10.1016/j.nhres.2023.10.001
Latest update: 20 Nov 2024
Short summary
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.
An innovative multi-strategy-mode waterlogging-prediction framework for predicting waterlogging...
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