Subgrid Informed Neural Networks for High-Resolution Flood Mapping DOI

Herath Mudiyanselage Viraj Vid Herath,

Lucy Marshall, Abhishek Saha

et al.

Published: Jan. 1, 2024

Language: Английский

Drowning overconfidence with uncertainty: mitigating deep learning overconfidence in flood depth super-resolution through maximum entropy regularization DOI
Maelaynayn El Baida, Farid Boushaba, Mimoun Chourak

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

Citations

0
Choice of Gaussian Process kernels used in LSG models for flood inundation predictions DOI
Jiabo Lu, Quan J. Wang, Niels Fraehr

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132949 - 132949

Published: Feb. 1, 2025

Language: Английский

Citations

0
Flo-Sr: Deep Learning-Based Urban Flood Super-Resolution Model DOI
Hyeonjin Choi,

Hyuna Woo,

Minyoung Kim

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Rapid flood inundation mapping by integrating deep learning-based image super-resolution with coarse-grid hydrodynamic modeling DOI Creative Commons
Wenke Song, Mingfu Guan, Kaihua Guo

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: March 25, 2025

Efficient and accurate flood inundation mapping is essential for risk assessment, emergency response, community safety. The deep learning-enabled rapid simulation demonstrates superior computational efficiency compared to traditional hydrodynamic models. However, most learning-based models currently focus on predicting the maximum water depth face challenges in generalizing rainfall events of different durations. This paper proposes a fast method based image super-resolution, utilizing novel DenseUNet architecture predict velocity temporal events. proposed integrates physical catchment characteristics enhance resolution maps generated by coarse-grid model using deep-learning model. applied rural-urban Shenzhen River southern China. effectively reproduces test against fine-grid model, achieving root mean square errors below 0.06 0.07 m/s, respectively, with percentage bias within ±5%. For prediction, exhibits Nash-Sutcliffe Pearson correlation coefficient exceeding 0.99. Similarly, both metrics exceed 0.94. outperforms over 2800 times. developed this study regression classification performance commonly used ResUNet UNet architectures. robust wide range super-resolution scale factors. presents an efficient surrogate mapping, providing valuable insights applying methods simulation.

Language: Английский

Citations

0
SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation DOI Creative Commons

Wenbin Song,

Mingfu Guan, Dapeng Yu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133280 - 133280

Published: April 1, 2025

Language: Английский

Citations

0
Urban Flood Modelling: Challenges and Opportunities - A Stakeholder-Informed Analysis DOI Creative Commons
Muhammad Qasim Mahmood, Xiuquan Wang, Farhan Aziz

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106507 - 106507

Published: April 1, 2025

Language: Английский

Citations

0
Subgrid informed neural networks for high-resolution flood mapping DOI Creative Commons
Herath Mudiyanselage Viraj Vidura Herath, Lucy Marshall, Abhishek Saha

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 660, P. 133329 - 133329

Published: April 28, 2025

Language: Английский

Citations

0
FLO-SR: Deep learning-based urban flood super-resolution model DOI Creative Commons
Hyeonjin Choi,

Hyuna Woo,

Minyoung Kim

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133529 - 133529

Published: May 1, 2025

Language: Английский

Citations

0
Riverine flood hazard map prediction by neural networks DOI Creative Commons
Zeda Yin, Arturo S. León

HydroResearch, Journal Year: 2024, Volume and Issue: 8, P. 139 - 151

Published: Oct. 30, 2024

Language: Английский

Citations

2
Optimized ensemble-based flood hazard mapping in low altitude subtropical riverine terrane DOI Creative Commons
Manish Pandey, Romulus Costache, Pratik Dash

et al.

Discover Geoscience, Journal Year: 2024, Volume and Issue: 2(1)

Published: Sept. 5, 2024

Language: Английский

Citations

1