Escape routes and safe points in natural hazards. A case study for soil

Engineering Geology, Journal Year: 2024, Volume and Issue: 340, P. 107683 - 107683

Published: Aug. 13, 2024

Language: Английский

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A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide

Landslides, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 14, 2024

Language: Английский

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Topographic stress proxy as a new causative factor in landslide susceptibility mapping

Gondwana Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 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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Short Paper: AI-Driven Disaster Warning System: Integrating Predictive Data with LLM for Contextualized Guideline Generation

Published: Dec. 19, 2024

Language: Английский

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Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3119 - 3119

Published: Aug. 23, 2024

Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods utilizing multi-source data and deep learning techniques to improve efficiency accuracy complex environments still focus research difficult issue research. In this study, we address above problems construct model based on shifted window (Swin) transformer. We chose Ya’an, which has terrain experiences frequent landslides, as study area. Our model, fuses features from different remote sensing sources introduces loss function that better learns boundary information target, is compared with pyramid scene parsing network (PSPNet), unified perception (UPerNet), DeepLab_V3+ models order explore potential test models’ resilience an open-source database. The results show Ya’an database, benchmark networks (UPerNet, PSPNet, DeepLab_v3+), Swin Transformer-based optimization improves overall accuracies by 1.7%, 2.1%, 1.5%, respectively; F1_score improved 14.5%, 16.2%, 12.4%; intersection over union (IoU) 16.9%, 18.5%, 14.6%, respectively. performance optimized excellent.

Language: Английский

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Mountain Landslide Risk Assessment Based on High Resolution and High Quality Dem from Airborne Lidar: A Case Study in Jiuzhaigou, Sichuan, China

Published: Jan. 1, 2024

Language: Английский

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Wildfire Risk Assessment to Overhead Transmission‐Line Based on Improved Analytic Hierarchy Process

Fire and Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

ABSTRACT The occurrence of wildfire disasters can easily trigger tripping in overhead transmission‐line, thereby posing a significant threat to the safe and stable operation power system. In order enhance prevention control capability risk assessment method based on improved analytic hierarchy process (AHP) is proposed. First, main factors are explored, indicator system for transmission‐line constructed. We propose novel runaway coefficient fire assessing impact sources disaster. Secondly, mutual information used avoid subjective arbitrariness AHP improve reliability each index weight. results show that about 82.14% new events 2023 Fujian (China) located medium‐, high‐, very‐high‐risk areas, demonstrating effectiveness proposed method. This methodology offers foundation mitigate wildfire.

Language: Английский

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