Yu Guo

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area.

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.

With the continuous expansion of coal-mining operations and the gradual progression into relatively deeper levels, numerous phenomena and issues distinct from those observed in shallow mining have emerged. These include roadway instability and rockbursts induced by medium to high in situ stresses, as well as alterations in underground stress fields and damage to rock mass structures caused by deep coal seam extraction. These developments present new challenges for strata pressure monitoring and management, water inrush prevention, and gas control. To investigate the distribution characteristics and evolution of mining-induced stress in the floor strata of coal seams, this study a mechanical model was established for mining-induced failure. An analytical solution for the mining-induced stress within the floor is derived. This solution is then applied to a detailed analysis of the engineering geological conditions and mining parameters of Panel 81307 in Baode Coal Mine. The principal conclusions are as follows: (1) Based on the theories of Material Mechanics and Elasticity, a mechanical model for mining-induced failure of the coal seam floor was constructed. The stress characteristics of a semi-infinite plane subjected to abutment pressure were analyzed, leading to an analytical solution for the mining-induced stress in the underlying rock mass. (2) Using Panel 81307 in the Shendong Mining Area’s Baode Coal Mine as a case study, computational procedures in MATLAB are employed to plot the relationship curves of horizontal stress ( σ _x ), vertical stress ( σ _y ), shear stress ( τ _xy) , maximum principal stress ( σ ₁ ), and minimum principal stress ( σ ₃ ) with respect to burial depth and face advance distance. The distribution patterns of stress within the coal seam floor were elucidated. By applying the Mohr-Coulomb failure criterion, the depth of mining-induced failure in the floor was determined to be 24 m. (3) Field observations have confirmed that the evolutionary patterns of mining-induced stress and the resulting failure depth in the floor strata align well with actual conditions, verifying the reliability of the proposed analytical solution for mining-induced failure.