Gully-type debris flow susceptibility assessment based on a multi-channel multi-scale residual network fusing multi-source data: a case study of Nujiang Prefecture

Abstract

ABSTRACTIn large-scale debris flow susceptibility assessments, there is often excessive manual intervention, low efficiency, and inadequate model accuracy. To address these issues, this paper integrates multiple data sources and proposes a Multi-channel and Multi-scale Residual Network (MMRNet) for automatic extraction of gully features. Firstly, MMRNet employs a multi-scale feature fusion module to capture both local and global features of gullies, enhancing the model’s feature representation capabilities. It then uses an improved residual structure to fuse shallow features, compress features, and improve assessment efficiency. Additionally, channel rearrangement techniques are used to enhance feature flow. Finally, susceptibility prediction is made based on the similarity between the gully under evaluation and gullies where debris flows have occurred. The natural breakpoint method is used to classify susceptibility results into five levels. Experimental results show that the very high susceptibility zones for debris flows are mainly concentrated in areas with abundant river systems along the Nujiang River, covering 61.68% of the entire study area, with a debris flow proportion of 98.78%. The MMRNet model achieves an accuracy (ACC) of 81.6% and an area under the curve (AUC) of 0.8320, indicating that this model is a high-performance method for debris flow susceptibility assessment.

Keywords

• Debris flow
• convolutional neural network
• multi-scale features
• deep learning
• machine learning