Preprints
https://doi.org/10.5194/nhess-2022-36
https://doi.org/10.5194/nhess-2022-36
 
04 Feb 2022
04 Feb 2022
Status: this preprint is currently under review for the journal NHESS.

A Multi-strategy-mode-waterlogging-prediction Framework for Urban Flood Depth

Zongjia Zhang1,2, Jun Liang2, Yujue Zhou3, Zhejun Huang2, Jie Jiang3, Junguo Liu4, and Lili Yang2 Zongjia Zhang et al.
  • 1School of Environment, Harbin Institute of Technology, Harbin, 150001, China
  • 2Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China
  • 3Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
  • 4School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China

Abstract. Flood is one of the most disruptive natural hazards, leading to massive loss of lives and considerable damage to properties. Coastal cities in Asia face floods almost every year due to monsoons influences. Early notification of flood incidents benefits the authorities and public to devise both short and long terms preventive measures, prepare evacuation and rescue missions, and relieve the flood victims. Based on time series prediction and machine learning regression algorithm, an innovative multi-strategy-mode-waterlogging-prediction framework for predicting waterlogging depth is proposed in this paper. The framework combines the historical rainfall and waterlogging depth to predict the near future waterlogging in time under future weather conditions. An expanding rainfall model was proposed to consider the positive correlation of future rainfall on the waterlogging. By selecting a suitable prediction strategy, adjusting the optimal model parameters, and then comparing the different algorithms, the optimal configuration of prediction is selected. In the actual value testing, the selected model has high computational efficiency, and the accuracy of predicting the waterlogging depth after 30 minutes can reach 86.1 %, which is superior to many data-driven prediction models for waterlogging depth. The framework is helpful to timely predict the depth of target point with a high level of accuracy. It’s of great significance to timely release early warning information to avoid casualties and property losses.

Zongjia Zhang et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on nhess-2022-36', DIANCHEN SUN, 28 Mar 2022 reply
    • AC1: 'Reply on CC1', Lili Yang, 05 Apr 2022 reply

Zongjia Zhang et al.

Zongjia Zhang et al.

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Short summary
An innovative multi-strategy-mode-waterlogging-prediction framework for predicting waterlogging depth is proposed in paper. The framework selects eight regression algorithms for comparison, and tests the prediction accuracy and robustness of the model under different prediction strategies. Finally, the accuracy of predicting water depth after 30 minutes can exceed 86. 1 %. It can provide decision-making reference to issue early warning information and command emergency dispatching in advance.
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