发表机构
International Institute of Information Technology Bangalore（班加罗尔国际信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对印度喀拉拉邦和尼泊尔，开发空间异质性感知框架，对比两种训练策略完成洪水-滑坡易感性与风险制图，发现跨区域学习增强区分性、区域约束学习保留环境差异，支持二者整合。
AI 中文摘要
洪水与滑坡常相伴发生，但它们与环境控制因子的关系存在空间异质性。本研究开发了一种空间异质性感知框架，用于印度喀拉拉邦和尼泊尔的洪水-滑坡易感性及相对风险制图。该框架将15km×15km的网格单元与区域特定的 Contextual Zones（ contextual 区域）相结合，对比了两种训练策略：邻近门控跨区域训练（S1）和生态门控区域约束训练（S2）。S1允许地理上邻近的模型跨越 Contextual 边界分配，而S2则将模型的开发与分配限制在同一区域内。针对每种灾害的随机森林模型采用策略特定的预测因子集，并通过空间留出的测试样本进行评估。易感性表面与CRITIC加权的暴露度和脆弱性指数相结合，生成特定灾害的九类双变量相对风险图。S1在两种灾害和两个区域中均实现了更高的平均准确率、精确率、召回率、F1值、AUC-ROC和PR-AUC，最大差异出现在尼泊尔洪水易感性中：AUC-ROC从S2下的0.728提升至S1下的0.886，PR-AUC从0.512提升至0.823。S2在尼泊尔两种灾害中产生更低的Brier分数，并保留了预测因子选择、SHAP排名和响应模式的区域特异性差异，尤其在喀拉拉邦表现明显。两种策略均重现了洪水易发的低地和滑坡易发的高地模式，但在易感性和风险类别上存在差异。喀拉拉邦的双变量风险图一致性为0.521，尼泊尔为0.711，在所有S1-S2对比中，分配分歧超过数量分歧。易感性与风险的对应关系保持在0.350以下，表明暴露度和脆弱性改变了优先级位置。总体而言，跨区域学习增强了区域区分能力，而区域约束学习保留了环境差异，支持二者的整合。
英文摘要
Floods and landslides often co-occur, but their relationships with environmental controls vary spatially. This study develops a spatial heterogeneity-aware framework for flood-landslide susceptibility and relative-risk mapping in Kerala, India, and Nepal. It combines 15 km x 15 km grid cells with region-specific contextual zones and compares proximity-gated cross-zone training (S1) and ecology-gated zone-constrained training (S2). S1 permits geographically nearby models to be assigned across contextual boundaries, whereas S2 restricts model development and assignment to the same zone. Random Forest models for each hazard use strategy-specific predictor sets and are evaluated on spatially held-out test samples. Susceptibility surfaces are integrated with CRITIC-weighted exposure and vulnerability indices to produce hazard-specific and nine-class bivariate relative-risk maps. S1 achieved higher mean accuracy, precision, recall, F1-score, AUC-ROC, and PR-AUC for both hazards and regions. The largest difference occurred for Nepal flood susceptibility, where AUC-ROC increased from 0.728 under S2 to 0.886 under S1 and PR-AUC from 0.512 to 0.823. S2 produced lower Brier scores for both Nepal hazards and retained zone-specific differences in predictor selection, SHAP rankings, and response patterns, particularly in Kerala. Both strategies reproduced flood-prone lowland and landslide-prone upland patterns but differed in susceptibility and risk classes. Bivariate risk-map agreement was 0.521 in Kerala and 0.711 in Nepal, with allocation disagreement exceeding quantity disagreement in all S1-S2 comparisons. Susceptibility-to-risk correspondence remained below 0.350, showing that exposure and vulnerability changed priority locations. Overall, cross-zone learning strengthens regional discrimination, while zone-constrained learning preserves environmental differences, supporting their integration.
