Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale

Qin, Shengwu; Qiao, Shuangshuang; Yao, Jingyu; Zhang, Lingshuai; Liu, Xiaowei; Guo, Xu; Chen, Yang; Sun, Jingbo

doi:10.1007/s11069-022-05487-5

Cited by 4 publications

(1 citation statement)

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“…Machine learning models such as LSTM, 18 CNN, 19 and DNN 20 are widely used to predict the development trend of debris flows. Scholars also combine the above models with GIS 21 and terrain data (DEM) 22 to achieve accurate prediction of debris flow movement. However, machine learning based methods mainly focus on numerical simulation or image processing, with less attention paid to the three‐dimensional morphological changes of debris flows, resulting in a single and insufficiently intuitive presentation form.…”

Section: Related Workmentioning

confidence: 99%

Two‐particle debris flow simulation based on SPH

Zhang,

Yang,

et al. 2024

Computer Animation & Virtual

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Debris flow is a highly destructive natural disaster, necessitating accurate simulation and prediction. Existing simulation methods tend to be overly simplified, neglecting the three‐dimensional complexity and multiphase fluid interactions, and they also lack comprehensive consideration of soil conditions. We propose a novel two‐particle debris flow simulation method based on smoothed particle hydrodynamics (SPH) for enhanced accuracy. Our method employs a sophisticated two‐particle model coupling debris flow dynamics with SPH to simulate fluid‐solid interaction effectively, which considers various soil factors, dividing terrain into variable and fixed areas, incorporating soil impact factors for realistic simulation. By dynamically updating positions and reconstructing surfaces, and employing GPU and hash lookup acceleration methods, we achieve accurate simulation with significantly efficiency. Experimental results validate the effectiveness of our method across different conditions, making it valuable for debris flow risk assessment in natural disaster management.

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