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-Reference-Cited by-同舟云学术

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

Published:2023-09-13 Issue:1 Volume:16 Page:3661-3679
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Melgar-García Laura¹,Martínez-Álvarez Francisco¹,Tien Bui Dieu²,Troncoso Alicia¹

Affiliation:

1. Data Science & Big Data Lab, Pablo de Olavide University, Seville, Spain

2. GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, Bø i Telemark, 3800, Norway

Funder

Spanish Ministry of Science and Innovation

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2252401

Reference73 articles.

1. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees

2. Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data

3. How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

4. Extreme rainfall affects assembly of the root-associated fungal community

5. Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments

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