Assessment of Eco-Geological Vulnerability Using Multiple Machine Learning Models: A Case Study of the Three Gorges Reservoir Area, China

Precise assessment of the vulnerability characteristics of human–land systems is es-sential for ensuring ecological security and sustainable development in regions affected by large-scale engineering projects. Using the Three Gorges Reservoir Area as a case study, this research develops a comprehensive evaluation index system based on a coupled framework of “Geo-environmental Background—Ecosystem Structure—Anthropogenic Perturbation.” By integrating deep neural networks (DNN), convolutional neural networks (CNN), and the analytic hierarchy process (AHP) with multi-source data, we perform a thorough assessment of eco-geological vulnerability. The results reveal the following key findings: (1) In eco-geological vulnerability assessment, deep learning methods (DNN and CNN) significantly outperform traditional AHP, with CNN showing superior precision and specificity due to its ability to extract local spatial features effectively, while DNN exhibits stronger overall robustness. (2) The spatial distribution of eco-geological vulnerability in the reservoir area is notably heterogeneous, with high and Extreme vulnerability zones concentrated along the main riverbanks, major tributary estuaries, and urban peripheries. These zones are strongly coupled with steep terrain, erodible lithology, high geological hazard risks, and intensive human activity. (3) Although the overall vulnerability remains relatively stable, local sensitivity is increasing. Ecological restoration projects in mountainous regions have effectively mitigated vulnerability in the hinterlands, while rapid urbanization has exacerbated vulnerability in emerging urban areas. The study concludes that the spatial pattern of vulnerability is primarily influenced by the geological–ecological background, with human disturbance—especially land use intensity—acting as the primary driver of vulnerability dynamics and local hotspots of high vulnerability. Based on these findings, we recommend a differentiated management approach tailored to eco-geological units: for high and extreme vulnerability zones along river and urban corridors, efforts should focus on spatial constraints and systemic resto-ration; for low and negligible vulnerability zones in mountainous areas, strategies should aim to enhance ecosystem quality and stability, thus fostering a coordinated regional ecological security framework.