In this project, 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.
For more information, see this paper.
Xu, K., Han, Z., Bin, L. et al. Rapid forecasting of compound flooding for a coastal area based on data-driven approach. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06846-0