Rock Slope Stability Prediction: A Review of Machine Learning Techniques

Geotechnical and Geological Engineering, Год журнала: 2025, Номер 43(3)

Опубликована: Фев. 18, 2025

Язык: Английский

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Surrogate modeling for slope stability: integrating limit equilibrium method with machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(5)

Опубликована: Апрель 5, 2025

Язык: Английский

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Interpretability study of earthquake-induced landslide susceptibility combining dimensionality reduction and clustering

Frontiers in Earth Science, Год журнала: 2025, Номер 13

Опубликована: Апрель 25, 2025

An earthquake of magnitude Ms5.8 struck Barkam City, Aba Prefecture, Sichuan Province, China, on the morning 10 June 2022. This was followed by two additional earthquakes magnitudes Ms6.0 and Ms5.2. The triggered significant geological hazards, impacting City surrounding areas. Using Random Forest (RF) Extreme Gradient Boosting (XGBoost) machine learning models, we assessed landslide susceptibility in identified key influencing factors. study applied SHAP method to evaluate importance various factors, used UMAP for dimensionality reduction, employed HDBSCAN clustering algorithm classify data, thereby enhancing interpretability models. results show that XGBoost outperforms RF terms accuracy, precision, recall, F1 score, KC, MCC. primary factors occurrence are topographic features, seismic activity, precipitation intensity. research not only introduces innovative techniques methods analysis but also provides a scientific foundation emergency response post-disaster planning related risks following City.

Язык: Английский

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An innovative machine learning approach for slope stability prediction by combining shap interpretability and stacking ensemble learning

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

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Spatiotemporal Analysis of thermal islands in a Semi-Arid City: A Case Study of Kermanshah, Iran Using Machine Learning and Remote Sensing

Environmental Challenges, Год журнала: 2025, Номер unknown, С. 101174 - 101174

Опубликована: Май 1, 2025

Язык: Английский

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Automated feature engineering for automated machine learning

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113671 - 113671

Опубликована: Май 1, 2025

Язык: Английский

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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)

Geocarto International, Год журнала: 2024, Номер 39(1)

Опубликована: Янв. 1, 2024

Язык: Английский

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

Sustainability, Год журнала: 2024, Номер 16(22), С. 9791 - 9791

Опубликована: Ноя. 9, 2024

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

Язык: Английский

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Deformation features of the Shenjiagou landslide before and after the impoundment of the Baihetan Reservoir, Southwest China

Landslides, Год журнала: 2024, Номер unknown

Опубликована: Дек. 23, 2024

Язык: Английский

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