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

Abstract

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.

Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example

“…Among the prevalent machine learning models, the support vector machine (SVM) often overfits when feature count substantially exceeds the sample size, and it also lacks direct weight provision and faces challenges in visualizing data in high dimensions [ 16 , 17 , 18 ]. In contrast, the logistic regression model typically underperforms, with generally low accuracy [ 19 , 20 , 21 ], while the random forest algorithm, benefiting from low data requirements and gradient boosting, mitigates overfitting to a degree [ 22 , 23 , 24 , 25 ]. The application of random forest (RF), SVM, and naive bayes (NB) methods in constructing a geohazard susceptibility assessment model for the Puge section of the Zemu River Valley in the Liangshan Yi autonomous prefecture has demonstrated superior RF model accuracy [ 26 ].…”