Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database

Lei, Dongze; Ma, Junwei; Zhang, Guangcheng; Wang, Yankun; Deng, Xin; Liu, Jiayu

doi:10.1007/s11069-024-06917-2

Nat Hazards

2024

DOI: 10.1007/s11069-024-06917-2

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Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database

Dongze Lei,

Junwei Ma,

Guangcheng Zhang

et al.

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Cited by 2 publications

References 84 publications

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Enhancing deep learning-based landslide detection from open satellite imagery via multisource data fusion of spectral, textural, and topographical features: a case study of old landslide detection in the Three Gorges Reservoir Area (TGRA)

Ren,

Ma,

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et al. 2024

Geocarto International

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Enhancing deep learning-based landslide detection from open satellite imagery via multisource data fusion of spectral, textural, and topographical features: a case study of old landslide detection in the Three Gorges Reservoir Area (TGRA)

Ren,

Ma,

Liu

et al. 2024

Geocarto International

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Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet

Tan,

Chen,

Liu

et al. 2024

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Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images.

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Bayesian ensemble learning and Shapley additive explanations for fast estimation of slope stability with a physics-informed database

Cited by 2 publications

References 84 publications

Enhancing deep learning-based landslide detection from open satellite imagery via multisource data fusion of spectral, textural, and topographical features: a case study of old landslide detection in the Three Gorges Reservoir Area (TGRA)

Enhancing deep learning-based landslide detection from open satellite imagery via multisource data fusion of spectral, textural, and topographical features: a case study of old landslide detection in the Three Gorges Reservoir Area (TGRA)

Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet

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