Susceptibility Analysis of Glacier Debris Flow by Investigating the Changes in Glaciers Based on Remote Sensing: A Case Study

Lin, Ruoshen; Mei, Gang; Liu, Ziyang; Xi, Ning; Zhang, Xiaona

doi:10.3390/su13137196

Cited by 7 publications

(6 citation statements)

References 55 publications

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“…As illustrated in Figure 11, the moraines caused by ice avalanches and avalanches are very likely to lead the ice floods, landslide, glacial debris flow, and other glacial disasters, which causes serious damage to the bridges, road, etc. The occurrence of glacial hazards has its spatiotemporal distribution pattern, occurrence mechanism, and disaster process (Lin et al, 2021). In general, the warming and wetting of the climate lead to the increase of glacier instability, which in turn leads to the occurrence of glacier disaster risk.…”

Section: Resultsmentioning

confidence: 99%

Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks

Lin

Mei

2022

Geological Journal

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Debris‐covered glacier mapping for monitoring glacier fluctuations is necessary to prevent geohazards caused by glaciers. Recently, deep learning‐based methods have been widely utilized for the identification of debris‐covered glaciers. Compared with conventional geospatial methods, deep learning‐based approaches have the advantage of large‐scale coverage and outstanding accuracies in identifying glaciers. However, there are two main difficulties when using deep learning‐based approaches: (1) object misclassification, which causes the misclassifications of the surrounding bedrock, snow, and mountain‐shadowed areas as glaciers; and (2) the inaccurate segmentation of boundaries. In addition, the sample sets for training deep learning models are generally insufficient, which leads to unsatisfactory results. To address the above problems, a deep learning‐based approach is proposed for accurate and automatic mapping of the complex debris‐covered glacier from remote sensing imagery for glacial hazard prevention. First, high‐quality remote sensing imagery is acquired and pre‐processed. Second, we adopt a weight‐optimized glacier semantic segmentation model to our approach. Then, the post‐processing procedures can be used to obtain the glacier outline. Finally, an accurate and automatic mapping of complex debris‐covered glaciers can be achieved. To demonstrate the effectiveness, our approach is applied to observe spatiotemporal changes in the glacier outline in the Nyenchen Tanglha region. Results show that most evaluation metrics of our model are higher than 90%, which demonstrates that it is reasonable for the glacier boundary extraction. We also verify that the proposed approach can be used to observe the changes in glaciers to prevent and control glacial hazards in high‐mountain regions.

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Section: Resultsmentioning

confidence: 99%

Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks

Lin

Mei

2022

Geological Journal

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“…Among the two papers using feature weighting, the algorithms utilized were certainty factor and genetic algorithm. Among the 46 studies using model comparison, the number of papers comparing ML models (36) was significantly higher than that of those comparing ML models and non-ML models (10), as shown in Figure 8. Among the 36 papers comparing ML models, the number of papers comparing shallow ML models (32) was much higher than that of those comparing shallow ML models and deep learning models (4).…”

Section: Prediction Performance Improvement Strategiesmentioning

confidence: 96%

“…Hazard, on the other hand, incorporates triggering factors like rainfall into the susceptibility assessment. Debris flow prediction models fall into four main categories: knowledge-driven models (analytic hierarchy process [31,32], rainfall threshold method [33][34][35], geomorphic information entropy [36], etc. ), traditional statistical models (weight of evidence method [37,38], certainty factor [39,40], frequency ratio [40,41], etc.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Yang,

Ge,

Chen

et al. 2024

Water

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Machine learning (ML) has become increasingly popular in the prediction of debris flow occurrence, but the various ML models utilized as baseline predictors reported in previous studies are typically limited to individual case bases. A comprehensive and systematic evaluation of existing empirical evidence on the utilization of ML as baseline predictors for debris flow occurrence is lacking. To address this gap, we conducted a meta-analysis of ML-based prediction modeling of debris flow occurrence by retrieving papers that were published between 2000 and 2023 from the Scopus and Web of Science databases. The general findings were as follows: (1) A total of 84 papers, distributed across 37 different journals in this time period, reflecting an overall upward trend. (2) Debris flow disasters occur throughout the world, and a total of 13 countries carried out research on the prediction of debris flow occurrence based on ML; China made significant contributions, but more research efforts in African countries should be considered. (3) A total of 36 categories of ML models were utilized as baseline predictors for debris flow occurrence, with logistic regression (LR) and random forest (RF) emerging as the most popular choices. (4) Feature engineering and model comparison were the most commonly utilized strategies in predicting debris flow occurrence based on ML (53 and 46 papers, respectively). (5) Interpretation methods were rarely utilized in predicting debris flow occurrence based on ML, with only 16 papers reporting their utilization. (6) In the prediction of debris flow occurrence based on ML, interpretation methods were rarely utilized, searching by data materials was the most important sample data source, the topographic factors were the most commonly utilized category of candidate variables, and the area under the ROC curve (AUROC) was the most frequently reported evaluation metric. (7) LR’s prediction performance for debris flow occurrence was inferior to that of RF, BPNN, and SVM; SVM was comparable to RF, and all superior to BPNN. (8) The application process for the prediction of debris flow occurrence based on ML consisted of three main steps: data preparation, model construction and evaluation, and prediction outcomes. The research gaps in predicting debris flow occurrence based on ML include utilizing new ML techniques and enhancing the interpretability of ML. Consequently, this study contributes both to academic ML research and to practical applications in the prediction of debris flow occurrence.

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“…As for the method of remote sensing image susceptibility analysis, Lin et al considered the influence of the change in glacier ablation volume and conducted a mudslide susceptibility analysis using the G217 glacier mudslide-prone trench on the Dukku Highway in Xinjiang. This study showed that accurate prediction of glacier mudslide susceptibility could be achieved using high-resolution remote sensing image data and machine learning algorithms [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section

Chen

Hong

Han

et al. 2023

Sensors

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Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.

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Susceptibility Analysis of Glacier Debris Flow by Investigating the Changes in Glaciers Based on Remote Sensing: A Case Study

Cited by 7 publications

References 55 publications

Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks

Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section

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