AI 中文总结
本研究针对数据稀缺下自然灾害易发性制图的问题，提出SAGE框架结合XGBoost构建SAGE-XGBoost模型，经滑坡、野火案例验证，其性能优于传统空间机器学习模型，为地理空间预测提供高效可迁移方案。
AI 中文摘要
自然灾害易发性制图常受限于标注数据有限，这会降低传统机器学习的泛化能力，并限制复杂深度学习模型的适用性。本研究提出SAGE（空间增强图嵌入），这是一种结构信息特征工程框架，它将基于受控噪声的数据增强与基于邻域的图嵌入相结合，以在数据稀缺条件下提升预测性能。研究人员构建了一个K近邻图来推导局部空间统计量，通过主成分分析对这些统计量进行降维，并将其与环境协变量和空间坐标相融合。所得特征被用于结合XGBoost开发SAGE-XGBoost模型。该框架针对滑坡和野火易发性制图进行了评估，SAGE-XGBoost的表现始终优于传统及空间显式机器学习模型；与Spatial XGBoost相比，在两个案例研究中其绝对提升幅度超过33个百分点，模型在滑坡易发性上的AUC值约为0.97，在野火易发性上的AUC值约为0.95。特征重要性分析证实了图嵌入对预测的贡献，同时图嵌入的集成提升了空间一致性并减少了局部噪声放大。总体而言，SAGE-XGBoost为深度学习表示学习提供了一种高效且可迁移的替代方案，适用于有限监督下的环境灾害评估及其他地理空间预测任务。
英文摘要
Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models. This study proposes SAGE (Spatially Augmented Graph Embeddings), a structurally informed feature-engineering framework that combines controlled noise-based data augmentation with neighborhood-based graph embeddings to improve prediction under data-scarce conditions. A K-nearest neighbor graph is constructed to derive local spatial statistics, which are reduced using principal component analysis and integrated with environmental covariates and spatial coordinates. The resulting features are used with XGBoost to develop the SAGE-XGBoost model. The framework was evaluated for landslide and wildfire susceptibility mapping. SAGE-XGBoost consistently outperformed conventional and spatially explicit machine learning models. Compared with Spatial XGBoost, it achieved an absolute improvement of above 33 percentage points across the two case studies. The model reached AUC values of approximately 0.97 for landslide susceptibility and 0.95 for wildfire susceptibility. Feature importance analysis confirmed the contribution of graph embeddings to prediction, while their integration improved spatial coherence and reduced local noise amplification. Overall, SAGE-XGBoost provides an efficient and transferable alternative to deep representation learning for environmental hazard assessment and other geospatial prediction tasks under limited supervision.
