Assessment of karst collapse susceptibility in Shichang Township, Jinsha County, Bijie City based on GCN model

Abstract

[Objective] To address the deficiencies in spatial feature capturing and nonlinear fitting in current regional karst collapse susceptibility studies, a modeling framework based on graph convolutional networks(GCN) is proposed. [Methods] The fractal characteristics of karst development were integrated to quantitatively evaluate the degree of karst development. Shichang Township in Bijie City, Guizhou Province, was selected as the study area. 16 disaster-inducing factors were selected to establish a GCN-based collapse susceptibility evaluation model, and the regional karst collapse susceptibility was investigated by comparing the GCN model with support vector machine(SVM) and random forest(RF) models. [Results] The result showed that different strata in the same karst area had varying impacts on karst collapse. Converting complex karst morphologies into fractal dimensions enabled the quantitative assessment of karst development degree, effectively avoiding subjective judgment bias. All three evaluation models mitigated the issue of insufficient nonlinear fitting, and the orientation of very high/high susceptibility zones was roughly consistent with the trend of karst troughs in the study area, and the models exhibited both similarities and differences. Among them, the accuracy, sensitivity, specificity, precision, and AUC of the GCN model were significantly higher than those of the other two models. The very high susceptibility zones identified by the GCN model displayed a continuous strip-like distribution, which aligned with the orientation of the karst troughs in the study area, with an overlap rate of 92%. Moreover, the GCN model concentrated 33 hazard points(accounting for 75%) in these very high susceptibility zones, while the area of these zones only accounted for 18.18% of the total area, demonstrating the model's strong capability to identify the clustering of hazard points in high susceptibility zones. [Conclusion] By integrating the fractal characteristics of karst development, the GCN model accurately identifies the very high susceptibility zones in the central and western parts of the study area, and the spatial distribution is fully consistent with the strong karst development zones. Compared with traditional single-indicator evaluation, the model captures the spatial heterogeneity of collapse risks more effectively. The findings enhance the understanding of the karst development degree in Shichang Township, Jinsha County, Bijie City. Additionally, a relatively suitable evaluation system for karst collapse susceptibility is developed. The GCN model demonstrates more prominent performance in regional karst collapse susceptibility evaluation.

Keywords

karst development degree / depression density / fractal characteristics / machine learning / susceptibility evaluation / geological hazards / influencing factors / graph convolutional networks