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Debris flow susceptibility mapping in alpine canyon region: a case study of Nujiang Prefecture

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Abstract

Accurate debris flow susceptibility mapping (DFSM) plays a crucial role in enabling government authorities to devise rational policies to mitigate the threats posed by debris flows to human life and property. Nujiang Prefecture, located in the alpine canyon region, is prone to frequent debris flows in China. Therefore, this study focuses on Nujiang Prefecture as the research area. Based on the characteristics of debris flow development, the occurrence mechanism, and the actual conditions of the study area, small watersheds are selected as mapping units. Fifteen influencing factors, including elevation, slope, aspect, relief, surface roughness, Melton ratio, NDVI, lithology, distance to faults, rainfall, SPI, TWI, STI, watershed aera, and gully density, are considered in the mapping process. We explored the predictive performance of three single models, namely, the statistical model certainly factor (CF), the machine learning model support vector machines (SVM), and the deep learning model convolutional neural network (CNN). Additionally, we investigated the coupling models CF-LR (statistical model coupled with machine learning model) and CNN-SVM (machine learning model coupled with deep learning model) in the mapping of debris flow sensitivity. The analysis and comparison of model performance were conducted using the area under the receiver operating characteristic curve (AUC) and the mean value (MV) and standard deviation (SD) of debris flow sensitivity values. The results demonstrate that all five models show promising performance in DFSM. Among them, the CNN-SVM coupled model (AUC = 0.933, MV = 0.211, SD = 0.199) outperforms the others, exhibiting the best predictive capability. These findings can serve as valuable references for debris flow prevention and control efforts.

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Funding

This research was supported by the Yunnan Provincial Science and Technology Department-Yunnan University Joint Fund Key Projects(Grand no. 2019FY003017), National Natural Science Foundation of China(Grand no. 41161070), and International Laboratory for Remote Sensing of Natural Resources in China, Lao People’s Democratic Republic, Bangladesh, and Myanmar.

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Authors and Affiliations

  1. School of Earth Sciences, Yunnan University, Kunming, 650500, China

    Yimin Li, Wenxue Jiang, Shengbin Lv & Enhua Ma

  2. Yunnan Colleges and Universities Domestic High Score Satellite Remote Sensing Geological Engineering Research Center, Kunming, 650500, China

    Yimin Li

  3. Institute of International Rivers and Eco-Security, Yunnan University, Kunming, 650500, China

    Xianjie Feng & Wenxuan Yu

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  1. Yimin Li
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  2. Wenxue Jiang
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  3. Xianjie Feng
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  4. Shengbin Lv
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  5. Wenxuan Yu
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  6. Enhua Ma
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Contributions

Yimin Li and Wenxue Jiang conceived the idea of this paper. Xianjie Feng, Shengbin Lv, Wenxuan Yu, and Enhua Ma completed the material preparation and model training, and the paper was written by Yimin Li and Wenxue Jiang. All authors commented on the research and agreed to the submission of the final manuscript.

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Correspondence to Wenxue Jiang.

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Li, Y., Jiang, W., Feng, X. et al. Debris flow susceptibility mapping in alpine canyon region: a case study of Nujiang Prefecture. Bull Eng Geol Environ 83, 169 (2024). https://doi.org/10.1007/s10064-024-03657-2

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