Published August 15, 2024
| Version v1
Dataset
Open
Rapid mapping of flood inundation by deep learning-based image super-resolution
Authors/Creators
Description
# Rapid mapping of flood inundation by deep learning-based image super-resolution
# Developer: Wenke Song
# The University of Hong Kong
# Contact email: songwk@connect.hku.hk
# MIT License
# Copyright (c) 2024 songwk0924
There are two folders in the compressed file: Trained_model and Test_cases:
(1) Trained_model
model_d_DenseUnet.pth, for predicting the maximum water depth;
model_v_DenseUnet.pth, for predicting the maximum velocity.
(2) Test_cases
Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;
Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3;
bathy_mat_5m_0p.csv: Elevation data to create mask layer;
Fine_grid_flood_maps (2DSWEs):
hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;
velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;
The aforementioned data will be used as input for model prediction (Prediction.py).