A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area

Figures & data

Figure 1. Location of the Phu Tho province and flooded locations.

Table 1. Accuracy and loss results for the selected parameters for each model.

	Training phase		Validating
Model	Loss value	Accuracy	Loss value	Accuracy
U-Net	0.14221	0.95728	0.15105	0.95717
WU-Net	0.15989	0.95726	0.16711	0.95718
U-Net++	0.19395	0.95726	0.21418	0.95718

Table 2. Accuracy and loss results obtained for each model in the evaluation phase.

Model	Training+validation loss	Training+validation accuracy	Test loss	Test accuracy
U-Net	0.14506	0.95725	0.13963	0.96051
WU-Net	0.15743	0.95723	0.14051	0.96049
U-Net++	0.19731	0.95724	0.18302	0.96049

Figure 3. Flash-flood susceptibility map using deep learning methods. (a) U-Net model; (b) WU-Net model and (c) U-Net++ model.

Figure 4. Flood susceptibility map of U-Net divided in four ranges of probability (20%, 40%, 60% and 80%).

Figure 5. Flood susceptibility map of U-Net divided in four ranges of probability (5%, 10%, 15% and 20%).

Figure 6. Flood susceptibility map of WU-Net divided in four ranges of probability (20%, 40%, 60% and 80%).

Figure 7. Flood susceptibility map of U-Net++ divided in four ranges of probability (20%, 40%, 60% and 80%).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.