A Machine Learning-Based Study on Ecological Sensitivity and Its Driving Factors in the Karst Landforms of Yangshuo County, Guilin

Yu-Tian Zhao^1*Yi-zhu Wang²Xiao-li Liu³Yu Shi⁴Jing Yang^1*

• ¹College of Art and Design, Dalian Polytechnic University, Dalian, China
• ²College of art and design, Beijing University of Technology, Beijing, China
• ³Independent Researcher, Jinan, China
• ⁴Nanjing University, Nanjing, China

Karst landscapes are characterized by fragile ecosystems due to their shallow soil layers, unique hydrological structures, and high habitat heterogeneity. The driving mechanisms of ecological processes within these systems are highly complex. Traditional evaluation methods (e.g., AHP, PCA), which are often based on linear assumptions, struggle to effectively capture complex mechanisms such as non-linearity and high-order interactions within multi-factor interactions. This results in limited capacity for identifying and interpreting the driving factors of ecological sensitivity. Scientifically assessing this sensitivity is crucial for achieving regional sustainable development. This study takes Yangshuo County, Guilin—a typical karst area—as a case study. It introduces the Self-Organizing Map (SOM), Random Forest (RF) model and the SHAP (SHapley Additive exPlanations) interpretability framework to evaluate ecological sensitivity based on a synthesis of 11 factors, including lithology, rocky desertification, and slope gradient. The results indicate that: (1) The ecological sensitivity in Yangshuo County can be classified into five distinct levels, predominantly dominated by vegetation-type sensitive areas and valley cultivated land sensitive areas. (2) The Random Forest model identified natural baseline factors, such as lithology, rocky desertification, and slope gradient, as the key drivers. (3) SHAP analysis further revealed non-linear interaction mechanisms among these factors. Crucially, it identified a "geological baseline - topo-graphic dynamics - ecological process" cascading effect. This includes interactions such as steep slopes amplifying rocky desertification risks, the blocking effect of vegetation at the critical threshold of desertification, and the superposition and modification of natural factor influences by human activities. The "Self-Organizing Map-Random Forest-SHAP" (SOM-RF-SHAP) evaluation framework developed in this study provides a novel methodology for quantifying the complex This is a provisional file, not the final typeset article driving mechanisms of ecological sensitivity in karst regions.The findings of-fer a scientific basis for ecological conservation and spatial planning in Yangshuo County and similar areas.