Assessing the environmental impacts of flooding in Brazil using the flood area segmentation network deep learning model
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06914-5.pdf
Reference28 articles.
1. Alimonti G, Mariani L (2024) Is the number of global natural disasters increasing? Environ Hazards 23(2):186–202. https://doi.org/10.1080/17477891.2023.2239807
2. Al-Ruzouq R, Shanableh A, Jena R, Gibril MBA, Hammouri NA, Lamghari F (2024) Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model. Geosci Front 15(3):101780. https://doi.org/10.1016/j.gsf.2024.101780
3. The Center for Disaster Philanthropy (2024) https://disasterphilanthropy.org/disasters/2024-rio-grande-do-sul-brazil-floods/. Acessed 08 May 2024
4. CNN World (2024) https://edition.cnn.com/2024/05/09/world/brazil-floods-death-toll-intl-latam/index.html. Accessed 09 May 2024
5. Dataset (2023) https://www.kaggle.com/datasets/faizalkarim/flood-area-segmentation?select=Mask. Accessed 31 Jan 2023
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