Rapid forecasting of compound flooding for a coastal area based on data-driven approach

Kui Xu, Zhentao Han, Lingling Bin (), Ruozhu Shen and Yan Long
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Kui Xu: Tianjin University
Zhentao Han: Tianjin University
Lingling Bin: Tianjin Normal University
Ruozhu Shen: Ningxia Capitalwater Sponge City Construction & Development, Ltd
Yan Long: Hebei University of Engineering

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 2, No 9, 1399-1421

Abstract: Abstract The scenarios when heavy rainfall and high tides occur in succession or simultaneously can lead to compound flooding. Compound floods exhibit greater destructiveness than floods caused by one driver in coastal cities. Prediction for compound floods with real-time and high accuracy can contribute to mitigating the losses caused by floods. However, existing rapid forecasting studies neglect the compound impact of rainfall and tides in coastal floods. In this study, the information on rainfall and tides is utilized as input features to capture the drivers of compound flooding. To reduce the risk of overfitting, the light gradient boosting machine (LightGBM) is employed for feature selection. The one-dimensional convolutional neural network (CNN) is then trained on the reduced-dimensionality data. Hence, we construct LightGBM-CNN to predict flood distribution in coastal cities. The model is applied on Haidian Island, Hainan Province, China. The results indicate that incorporating rainfall and tides as input features significantly reduced the mean absolute error (MAE) from 0.179 to 0.044 and the root mean square error (RMSE) from 0.223 to 0.101, compared to using rainfall as input features. Compared to the CNN without feature selection using LightGBM, the performance of LightGBM-CNN has shown a significant improvement. The results suggest that the LightGBM-CNN offers a foundational reference for compound flood forecasting in coastal cities.

Keywords: Coastal urban flooding; Compound flooding; Data-driven model; Convolutional neural network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-06846-0

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