Cited by Summary of environmental effect and seismic activity in Qinghai-Tibet Plateau

The Seismic Dynamic Response Characteristics of the Steep Bedding Rock Slope Are Investigated Using the Hilbert–Huang Transform and Marginal Spectrum Theory DOI

Zhuan Li, Longfei Li, Kun Huang

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3078 - 3078

Published: March 12, 2025

The steep bedding rock slope (SBRS) is easily destabilized under earthquake action, so it crucial to research the features of this kind slope’s seismic dynamic reactions in order prevent and mitigate disasters. Few researchers have examined these slopes from an energy perspective, majority recent focuses on displacement acceleration response patterns kinds action. This work performed extended study a numerical simulation systematically analyzed characteristics type earth quake conditions standpoint utilizing Hilbert–Huang transform (HHT) marginal spectrum (MSP) theory. was carried out shaking table test our previous work. findings indicate following: (1) ‘elevation effect’ ‘surface are clearly seen amplification factor (AAF) during earthquake. selectivity acceleration’s Fourier impact indicates that elevation effect makes high-frequency peak’s amplitude more noticeable. (2) Although weak layer pronounced portion, both affect wave’s Hilbert energy. As result, at top usually destroyed first (3) Prior locked segment’s penetration failure toe SBRS, band monitoring point portion segment will rise sharply. suggests upper has begun sustain damage. There antecedents even when there no failure.

Language: Английский

Citations

Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms DOI

Bo Liu, Haijia Wen,

Mingrui Di

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

Citations

Refined Landslide Susceptibility Mapping Considering Land Use Changes and InSAR Deformation: A Case Study of Yulin City, Guangxi DOI

Pengfei Li,

Huini Wang,

Hongli Li

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3016 - 3016

Published: Aug. 17, 2024

Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban infrastructure planning. To address this, we proposed a refined method landslide assessment, which comprehensively considered both static dynamic factors. Neural network methods were analysis. Land cover (LULC) change InSAR deformation then integrated into the traditional zoning to obtain map with higher accuracy. Validation was conducted on improved using site data. The results showed that LULC proven be core driving factors occurrence study area. GRU model achieved highest performance (AUC = 0.886). introduction of surface data could rationalize inappropriateness zoning, correcting false positive negative areas caused human activities. Ultimately, 12.25% area high-susceptibility zones, 3.10% 0.74% being corrected. enabled analysis over large areas, providing technical support disaster prevention mitigation references geological hazard assessment

Language: Английский

Citations

Optimized Landslide Segmentation from Satellite Imagery Based on Multi-resolution Fusion and Attention Mechanism DOI

Yibo Ling,

Yuli Wang, Yi Lin

et al.

IEEE Geoscience and Remote Sensing Letters, Journal Year: 2024, Volume and Issue: 21, P. 1 - 5

Published: Jan. 1, 2024

Language: Английский

Citations

Assessment of Landslide Susceptibility using Geospatial Techniques: A Comparative Evaluation of Machine Learning and Statistical Models DOI

Subrata Raut,

Dipanwita Dutta, Debarati Bera

et al.

Geological Journal, Journal Year: 2024, Volume and Issue: 60(5), P. 1129 - 1149

Published: Nov. 20, 2024

This study delineates landslide susceptibility zones in the Kalimpong district by integrating multi‐sensor datasets and assessing effectiveness of statistical machine learning models for precision mapping. The analysis utilises a comprehensive geospatial dataset, including remote sensing imagery, topographical, geological, climatic factors. Four were employed to generate maps (LSMs) using 16 influencing factors: two bivariate models, frequency ratio (FR) evidence belief function (EBF) random forest (RF) support vector (SVM). Out 1244 recorded events, 871 events (70%) used training 373 (30%) validation. distribution classes predicted RF SVM produced similar distributions, predicting 13.30% 14.30% area as highly susceptible, 2.42% 2.82% very respectively. In contrast, FR model estimated 20.98% susceptible 4.30% whereas EBF 17.42% 5.89% these categories, Model validation receiver operating characteristic (ROC) curves revealed that (RF SVM) had superior prediction accuracy with AUC values 95.90% 86.60%, respectively, compared (FR EBF), which achieved 74.30% 76.80%. findings indicate Kalimpong‐I is most vulnerable, 6.76% its categorised high 24.80% susceptibility. Conversely, Gorubathan block exhibited least 0.95% 6.48% classified susceptibility, research provides essential insights decision‐makers policy planners landslide‐prone regions can be instrumental developing early warning systems, are vital enhancing community safety through timely evacuations preparedness measures.

Language: Английский

Citations

Probabilistic failure assessment of oil and gas gathering pipelines using LightGBM algorithm DOI

Xinhong Li,

Yabei Liu,

Renren Zhang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: unknown, P. 110747 - 110747

Published: Dec. 1, 2024

Language: Английский

Citations

Summary of environmental effect and seismic activity in Qinghai-Tibet Plateau DOI

Zhang Yanyun,

Zhang Yu,

Su Huazheng

et al.

Earthquake research advances, Journal Year: 2024, Volume and Issue: unknown, P. 100354 - 100354

Published: Dec. 1, 2024

Language: Английский

Citations