Convolutional Neural Network-Based Risk Assessment of Regional Susceptibility to Road Collapse Disasters: A Case Study in Guangxi

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3108 - 3108

Published: March 13, 2025

The Guangxi Zhuang Autonomous Region, a vital strategic geographic entity in southern China, is prone to frequent road collapse disasters due its complex topography and high rainfall, severely affecting regional economic social development. Existing risk assessments for these often lack comprehensive analysis of the combined influence multiple factors, their predictive accuracy requires enhancement. To address deficiencies, this study utilized ResNet18 model, convolutional neural network (CNN)-based approach, integrating 10 critical factors—including slope gradient, lithology, precipitation—to develop assessment model disasters. This predicts maps spatial distribution across Guangxi. results reveal that very high-risk areas span 49,218.94 km2, constituting 20.38% Guangxi’s total area, with disaster point density 8.67 per 100 km2; cover 56,543.87 representing 23.41%, 3.38 low-risk encompass 61,750.69 accounting 25.57%, 0.29 km2. receiver operating characteristic (ROC) curve yields an area under (AUC) value 0.7879, confirming model’s reliability assessing risk. establishes scientific foundation prevention mitigation offers valuable guidance similar regions.

Language: Английский

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Geospatial SHAP interpretability for urban road collapse susceptibility assessment: a case study in Hangzhou, China

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 15, 2025

Language: Английский

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

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: April 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.

Language: Английский

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A novel landslide susceptibility mapping method based on a backpropagation neural network algorithm with optimized non-landslide samples and hyperparameters

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 155, P. 111052 - 111052

Published: May 9, 2025

Language: Английский

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