Soil Erosion Type and Risk Identification from the Perspective of Directed Weighted Complex Network

Identifying the geographic distribution and erosion risks of various soil erosion regions are critical inputs to the implementation of extensive and effective land protection planning. To obtain more accurate and sufficient erosion information on a large scope, this paper introduced the complex network theory to quantitatively simulate the topographic spatial structure and topological relationship of the erosion area. The watershed was selected as the basic study unit and the directed weighted complex network (DWCN) of each watershed was constructed from DEM data. The directed weighted complex network factor (DWCNF) of each watershed was calculated by the DWCN. After combining DWCNFs with existing SEEF, the soil erosion types and risks of sample areas in the Chinese Loess Plateau were identified by the random forest model. The results show that in both typical and atypical sample areas, the identification performance of soil erosion by combining DWCNFs with existing SEEFs was performed better than that by employing only the DWCNFs or SEEFs dataset. It is suggested that the quantitative description of the spatial structure and topological relationship of the watershed from the perspective of a complex network contributes to obtaining more accurate soil erosion information. The DWCNF of structural entropy, betweenness centrality, and degree centrality were of high importance, which can reliably and effectively identify the types and risks of soil erosion, thus providing a broader factor reference for relevant research. The method proposed in this paper of vectoring terrain into complex network structures is also a novel sight for geological research under complex terrain conditions.

Automated summary

This study introduced the complex network theory to simulate the topographic spatial structure and topological relationship of erosion areas. By constructing directed weighted complex networks and combining existing erosion evaluation factors, the random forest model was used to identify soil erosion types and risks in the Chinese Loess Plateau. Results showed that combining complex network factors with existing evaluation factors improved the identification performance of soil erosion.