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

Zhang, Zongjia1,2; Liang, Jun2; Zhou, Yujue3; Huang, Zhejun2; Jiang, Jie3; Liu, Junguo4,5; Yang, Lili2

2022-12-22

10.5194/nhess-22-4139-2022

NATURAL HAZARDS AND EARTH SYSTEM SCIENCES   Impact Factor & Quartile

1561-8633

1684-9981

Flooding is one of the most disruptive natural disasters, causing substantial loss of life and property damage. Coastal cities in Asia face floods almost every year due to monsoon influences. Early notification of flooding events enables governments to implement focused preventive actions. Specifically, short-term forecasts can buy time for evacuation and emergency rescue, giving flood victims timely relief. This paper proposes a novel multi-strategy-mode waterlogging-prediction (MSMWP) framework for forecasting waterlogging depth based on time series prediction and a machine learning regression method. The framework integrates historical rainfall and waterlogging depth to predict near-future waterlogging in time under future meteorological circumstances. An expanded rainfall model is proposed to consider the positive correlation of future rainfall with 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 min can reach 86.1 %, which is superior to many data-driven prediction models for waterlogging depth. The framework is useful for accurately predicting the depth of a target point promptly. The prompt dissemination of early warning information is crucial to preventing casualties and property damage.

SCI

English

Corresponding

Key Technologies Research and Development Program["2018YFC0807000","2019YFC0810705"] ; National Outstanding Youth Science Fund Project of the National Natural Science Foundation of China[71771113] ; Shenzhen scientific research funding for postdocs stand out[K22627501] ; Shenzhen Science and Technology Plan platform and carrier special[ZDSYS20210623092007023] ; Shenzhen Science and Technology Program[KCXFZ20201221173601003]

Geology ; Meteorology & Atmospheric Sciences ; Water Resources

Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources

WOS:000902174800001

COPERNICUS GESELLSCHAFT MBH

GEOSCIENCES

Web of Science

1.Harbin Inst Technol, Sch Environm, Harbin 150001, Peoples R China
2.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
5.North China Univ Water Resources & Elect Power, Henan Prov Key Lab Hydrosphere & Watershed Water S, Zhengzhou 450046, Peoples R China

Zhang, Zongjia,Liang, Jun,Zhou, Yujue,et al. A multi-strategy-mode waterlogging-prediction framework for urban flood depth[J]. NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,2022,22(12).

Zhang, Zongjia.,Liang, Jun.,Zhou, Yujue.,Huang, Zhejun.,Jiang, Jie.,...&Yang, Lili.(2022).A multi-strategy-mode waterlogging-prediction framework for urban flood depth.NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,22(12).

Zhang, Zongjia,et al."A multi-strategy-mode waterlogging-prediction framework for urban flood depth".NATURAL HAZARDS AND EARTH SYSTEM SCIENCES 22.12(2022).