Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Pengfei Li, Tianchun Dong, Yanhe Wang, Jing Luo, Huini Wang () and Huarui Zhang
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Pengfei Li: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Tianchun Dong: China Railway Qinghai-Tibet Group Company, Ltd., Xining 810000, China
Yanhe Wang: Xining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810099, China
Jing Luo: State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Huini Wang: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China
Huarui Zhang: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China

Sustainability, 2024, vol. 16, issue 12, 1-20

Abstract: Climate warming has induced the thawing of permafrost, which increases the probability of thaw slump occurrences in permafrost regions of the Qinghai–Tibet Engineering Corridor (QTEC). As a key and important corridor, thaw slump distribution is widespread, but research into effectively using neural networks to predict thaw slumping remains insufficient. This study automated the identification of thaw slumps within the QTEC and investigated their environmental factors and susceptibility assessment. We applied a deep learning-based semantic segmentation method, combining U-Net with ResNet101, to high spatial and temporal resolution images captured by the Gaofen-1 images. This methodology enabled the automatic delineation of 455 thaw slumps within the corridor area, covering 40,800 km², with corresponding precision, recall, and F1 scores of 0.864, 0.847, and 0.856, respectively. Subsequently, employing a radial basis function neural network model on this inventory of thaw slumps, we investigated environmental factors that could precipitate the occurrence of thaw slumps and generated sensitivity maps of thaw slumps along the QTEC. The model demonstrated high accuracy, and the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve reached 0.95. The findings of the study indicate that these thaw slumps are predominantly located on slopes with gradients of 1–18°, distributed across mid-elevation regions ranging from 4500 to 5500 m above sea level. Temperature and precipitation were identified as the predominant factors that influenced the distribution of thaw slumps. Approximately 30.75% of the QTEC area was found to fall within high to extremely high susceptibility zones. Moreover, validation processes confirmed that 82.75% of the thaw slump distribution was located within areas of high or higher sensitivity within the QTEC.

Keywords: Qinghai–Tibet Engineering Corridor; susceptibility map; thaw slumps; remote sensing; deep learning; permafrost degradation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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