Treatment of a Landslide in Sichuan DOI
Yuping Zeng

Journal of industry and engineering management., Journal Year: 2024, Volume and Issue: 2(4), P. 28 - 32

Published: Dec. 1, 2024

The article provides a detailed description of the shape and deformation characteristics certain slope, analyzes harmfulness proposes support design scheme, explains construction techniques each sub item.

Language: Английский

SDCGAN: A CycleGAN-Based Single-Domain Generalization Method for Mechanical Fault Diagnosis DOI
Yu Guo, Xiangyu Li, Jundong Zhang

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110854 - 110854

Published: Jan. 1, 2025

Language: Английский

Citations

2
A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan DOI
Atefeh Ahmadi Dehrashid, Hailong Dong,

Marieh Fatahizadeh

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: March 21, 2024

Language: Английский

Citations

7
Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range DOI
Te Pei, Tong Qiu

Acta Geotechnica, Journal Year: 2024, Volume and Issue: 19(10), P. 6617 - 6641

Published: Aug. 13, 2024

Language: Английский

Citations

7
Investigating the efficacy of physics-based metaheuristic algorithms in combination with explainable ensemble machine-learning models for landslide susceptibility mapping DOI
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Rizwan Ali Naqvi

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

Citations

0
Complex cross-regional landslide susceptibility mapping by multi-source domain transfer learning DOI Creative Commons
Yan Su,

Fu J,

Xiaohe Lai

et al.

Geoscience Frontiers, Journal Year: 2025, Volume and Issue: unknown, P. 102053 - 102053

Published: April 1, 2025

Language: Английский

Citations

0
Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet DOI Creative Commons
Senwang Huang, Liping Chen

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: Aug. 30, 2024

Landslide susceptibility maps (LSMs) can play a bigger role in promoting the understanding of future landslides. This paper explores and compares capability three state-of-the-art bivariate models, namely frequency ratio (FR), statistical index (SI), weights evidence (WoE), with ensembles multivariate logistic regression (LR), for LSM part Tibet. Firstly, landslide inventory map 829 records is obtained from field surveys interpretation. Secondly, 15 conditioning factors (LCFs) are considered prepared multi-data sources. Subsequently, multicollinearity analysis conducted to calculate independence between different factors. Then, Information Gain Ratio method (IGR) performed confirm predictive ability LCFs. Finally, LSMs constructed by, SI, WoE, LR their combination through 12 preferred The performance methods validated compared term areas under receiver operating characteristic curve (AUC) measures. results this study indicate hybrid models FR-LR, WoE-LR SI-LR achieved higher AUC value than all corresponding single methods. ensemble frameworks well line distribution pattern historical landslides research area. Therefore, proposed high-performance expected provide useful reference hazard prevention similar areas.

Language: Английский

Citations

3
Evolution of landslide susceptibility in the Three Gorges Reservoir area over the three decades from 1991 to 2020 DOI Creative Commons
Jiahui Dong,

Jinrong Duan,

Runqing Ye

et al.

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

Published: Feb. 25, 2025

Language: Английский

Citations

0
Interpretable Landslide Susceptibility Evaluation Based on Model Optimization DOI Creative Commons
Haijun Qiu, Yao Xu, Bingzhe Tang

et al.

Land, Journal Year: 2024, Volume and Issue: 13(5), P. 639 - 639

Published: May 8, 2024

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain interpreting the predictions of ML models. To reveal response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how results are realized. This study focuses on Zhenba County Shaanxi Province, China, employing both Random Forest (RF) Support Vector (SVM) develop LSM models optimized through Search (RS). enhance interpretability, incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), Shapley Additive (SHAP). The RS-optimized RF demonstrated superior performance, achieving Area Under Curve (AUC) 0.965. identified NDVI distance from road important factors influencing landslides occurrence. plays a positive role occurrence this region, landslide-prone areas within 500 m road. These analyses indicate importance improved hyperparameter selection enhancing accuracy performance. provides valuable insights into LSM, facilitating deeper understanding formation mechanisms guiding formulation effective prevention control strategies.

Language: Английский

Citations

2
A Graph–Transformer Method for Landslide Susceptibility Mapping DOI Creative Commons
Zhang Qing, Yi He, Yalei Zhang

et al.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 14556 - 14574

Published: Jan. 1, 2024

Landslide susceptibility mapping (LSM) is of great significance for regional land resource planning and disaster prevention reduction. The machine learning (ML) method has been widely used in the field LSM. However, existing LSM model fails to consider correlation between landslide disaster-prone environment (DPE) lacks global information, resulting a high false alarm rate Therefore, we propose an with GraphTransformer that considers DPE characteristics information. Firstly, analysis importance are employed on nine contributing factors (LCFs), dataset generated by combining remote sensing image interpretation verification. Secondly, graph constrained similarity relationship constructed realize DPE. Then, Transformer module introduced construct Graph-Transformer Finally, analyzed, accuracy proposed compared evaluated. experimental results show effectively improves models weakens influence environmental differences models. Compared convolutional network (GCN), sample aggregate (GraphSAGE), attention (GAT) models, AUC value more than 2.05% higher under relationship. In addition, 8.8% traditional ML conclusion, our framework can get better evaluation most methods, its provide effective ways key technical support investigation control

Language: Английский

Citations

2
Hybrid method for rainfall-induced regional landslide susceptibility mapping DOI
Shuangyi Wu,

Huaan Wang,

Jie Zhang

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Language: Английский

Citations

2