Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms

Huang, Wubiao; Ding, Min; Li, Zhenhong; Yu, Junchuan; Ge, Daqing; Liu, Qi; Yang, Jing

doi:10.1016/j.catena.2022.106866

Cited by 46 publications

(12 citation statements)

References 53 publications

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“…This finding that shows the median slope is more prone to landsliding also can be validated and proved by Huang et al [83] who reported that too high and too low slopes are not prone to landslides. Distance to rivers is one of the most important factors that control slope instability.…”

Section: Discussionsupporting

confidence: 74%

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Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

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Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

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Section: Discussionsupporting

confidence: 74%

“…With increasing rainfall, the probability of landslide occurrence is increased. In other words, rainfall erodes and washed the topsoil of the slope surface and destroys the completeness between soil mass and rock and consequently decreasing the shear strength of the rock and soil mass increases the probability of landslide occurrence [83].…”

Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Shahabi¹,

Tavakolifar²,

Alizadeh³

et al. 2023

Preprint

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“…By training in large-scale parallel computing environments, DL models have the capacity to automatically capture complex relationships between landslides and landslide conditioning factors (LCFs) and extract high-dimensional landslide features. This advantage has encouraged researchers to apply DL models in LSM studies with notable successes [13]- [15]. However, the two most popular DL models currently: convolutional neural network (CNN) and Transformer, each have their own distinct advantages and limitations when it comes to landslide feature 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < extraction, which impacts their accuracy in LSM and restricts the potential for widespread application [16].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Considering Landslide Local-Global Features Based on CNN and Transformer

Zhao,

Chen,

Dou

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslide susceptibility mapping (LSM) is a crucial step in quantitatively assessing landslide risk, essential for geologic hazards prevention. With the rapid development of deep learning models, convolutional neural networks (CNN) and Transformer architectures have been applied to LSM. However, these models still face the challenges of suboptimal mapping accuracy and limited capacity for multi-level landslide features extraction. In this study, we present a CNN-Transformer Local-Global Feature Extraction Network (CTLGNet) that combines the strengths of both CNN and Transformer models to effectively extract both landslide local and global features. We apply this model to LSM in two regions: the Three Gorges Reservoir area and Jiuzhaigou. To begin, nine landslide conditioning factors are selected and analyzed to construct the landslide dataset for LSM. Subsequently, the dataset is randomly split into training, validation, and test datasets in a 6:2:2 ratio to attain LSM results. Then, CTLGNet is compared to CNN, residual neural network (ResNet), densely connected convolutional network (DenseNet), Vision Transformer (ViT), and Fractional Fourier Image Transformer (FrIT) using various evaluation metrics. The results

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“…In recent years, the escalating human engineering activities and everchanging global climatic conditions have led to a sharp increase in the frequency of old landslide reactivations. This phenomenon has resulted in significant harm to both human life and property safety, as well as the natural environment [3][4][5]. Therefore, to safeguard the safety of human lives and property, it is imperative to undertake extensive detection and monitoring of old landslides on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Li,

Ding,

Zhang

et al. 2024

Applied Sciences

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The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area.

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Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms

Cited by 46 publications

References 53 publications

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road

Landslide Susceptibility Mapping Considering Landslide Local-Global Features Based on CNN and Transformer

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

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