The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model

Karst collapse, a sudden geological hazard with complex mechanisms and low predictability, presents significant threats to urban safety and sustainable development by jeopardizing human lives and infrastructure. To address the limitations of conventional prediction methods, in this study, we introduce an enhanced predictive model, the improved sparrow search algorithm-optimized extreme learning machine (ISSA-ELM), for accurate karst-collapse susceptibility assessment. The methodology incorporates two key innovations: first, it applies a Singer chaotic mapping technique to enhance the sparrow search algorithm (SSA), effectively mitigating local optima entrapment by increasing population diversity and enhancing global search capabilities. Second, the optimized ISSA automatically adjusts the initial weights and thresholds of the ELM, whereas a five-fold cross-validation is used to determine the optimal hidden layer configuration, forming an adaptive and intelligent prediction framework. When validated against 20 datasets from a representative karst region, the proposed model achieved exceptional performance, with a mean absolute error (MAE) of 0.0544 and a coefficient of determination (R 2 ) of 0.9914, significantly surpassing the prediction accuracy of conventional ELM and SSA-ELM models. The results underscore the ISSA-ELM’s superior nonlinear fitting capability, enhanced generalization performance, and outstanding stability in practical engineering applications. This research offers a solid scientific foundation for risk classification and hazard mitigation strategies while introducing a novel methodological framework through the integration of innovative algorithms. The proposed technical pathway provides significant theoretical advancements and practical engineering values for geological disaster prediction systems.