Flo-Sr: Deep Learning-Based Urban Flood Super-Resolution Model

Published: Jan. 1, 2025

Language: Английский

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Landslide Identification from Post-Earthquake High-Resolution Remote Sensing Images Based on ResUNet–BFA

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 995 - 995

Published: March 12, 2025

The integration of deep learning and remote sensing for the rapid detection landslides from high-resolution imagery plays a crucial role in post-disaster emergency response. However, availability publicly accessible datasets specifically landslide remains limited, posing challenges researchers meeting task requirements. To address this issue, study develops releases dataset using Google Earth imagery, focusing on impact zones 2008 Wenchuan Ms8.0 earthquake, 2014 Ludian Ms6.5 2017 Jiuzhaigou Ms7.0 earthquake as research areas. contains 2727 samples with spatial resolution 1.06 m. enhance recognition, lightweight boundary-focused attention (BFA) mechanism designed Canny operator is adopted. This improves model’s ability to emphasize edge features integrated ResUNet model, forming ResUNet–BFA architecture identification. experimental results indicate that model outperforms widely used algorithms extracting boundaries details, resulting fewer misclassifications omissions. Additionally, compared conventional mechanisms, BFA achieves superior performance, producing recognition more closely align actual labels.

Language: Английский

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Interpretable physics‐informed graph neural networks for flood forecasting

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Abstract Climate change has intensified extreme weather events, with floods causing significant socioeconomic and environmental damage. Accurate flood forecasting is crucial for disaster preparedness risk mitigation, yet traditional hydrodynamic models, while precise, are computationally prohibitive real‐time applications. Machine learning surrogates, such as graph neural networks (GNNs), improve efficiency but often lack physical consistency interpretability. This paper introduces HydroGraphNet, a novel physics‐informed GNN framework that, the first time, integrates Kolmogorov–Arnold Network (KAN) to enhance model interpretability in unstructured mesh‐based forecasting. The embeds mass conservation laws into loss function, ensuring physically consistent predictions. Additionally, it employs an autoregressive encoder–processor–decoder architecture that captures spatiotemporal dynamics mitigating error accumulation over long horizons. Validation on data from White River near Muncie, Indiana, demonstrates 67% reduction prediction error, near‐zero balance 58% improvement critical success index major events compared baseline model. These results highlight potential of proposed advance improved

Language: Английский

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Integrating DEM and Deep Learning for Forested Terrain Analysis: Enhancing Fire Risk Assessment Through Mountain Peak and Water System Extraction in Chongli District

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 692 - 692

Published: April 16, 2025

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance Chongli District. Our combines DEM data Faster Regions Convolutional Neural Networks (Faster R-CNN) CNN-based methods, breaking through the limitations of traditional approaches rely on manual feature extraction. It capable automatically identifying critical features, such as mountain peaks water systems, higher accuracy efficiency. DEMs provide high-resolution topographical information, which models leverage accurately identify delineate key geographical features. results show integration significantly improves by offering detailed precise analysis, thereby providing more reliable inputs behavior prediction. The extracted fundamental prediction, enable accurate predictions spread potential impact areas. not only highlights great combining geospatial advanced machine but also offers scalable efficient solution forest mountainous regions. Future work will focus expanding dataset include environmental variables validating model different areas further its robustness applicability.

Language: Английский

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Modeling the geomorphologic susceptibility to debris flow initiation with geomorphometric parameters and fuzzy logic in the mountainous region of Serra do Mar (state of Paraná, Brazil)

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: May 6, 2025

Language: Английский

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