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对传统和高斯分析层次过程 (AHP) 方法进行比较分析,以评估山体滑坡易感性
Rômulo Marques-Carvalho1, André Carlos Ponce de Leon Ferreira de Carvalho2, Elton Vicente Escobar-Silva3
1Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, 13566-590, Brazil. romulomarques@usp.br.
Scientific reports
|November 1, 2025
概括
斯分析层次过程 (Gaussian AHP) 提高了山体滑坡易感度绘图的准确性和空间连贯性,相比传统的AHP. 这种方法提供了一种更客观,更可靠的方法来识别高风险的山体滑坡区域.
科学领域:
- 地质科学 地质科学
- 地理信息系统 (GIS) 是指地理信息系统.
- 自然危害评估自然危害评估
背景情况:
- 土地滑坡易感性测绘对于减少灾害风险至关重要.
- 传统的分析层次过程 (AHP) 方法在客观性和空间连贯性方面存在局限性.
- 地缘空间数据和分析技术的进步需要改进易感性建模.
研究的目的:
- 介绍和评估高斯分析层次流程 (高斯分析层次流程 (Gaussian AHP)) 用于土地滑坡易感性映射.
- 为了比较高斯 AHP 与传统 AHP 方法的性能.
- 评估通过这两种方法生成的易感性图的空间连贯性和不确定性.
主要方法:
- 斯AHP的应用用于山体滑坡易感性测绘.
- 使用2023年2月圣塞巴斯蒂亚诺事件的高分辨率山体滑坡清单进行验证.
- 使用ROC AUC,精度,平衡精度和灵敏度等指标进行性能评估.
- 不确定性分析和以密度为基础的评估滑坡分布.
主要成果:
- 与传统的AHP (23.52%) 相比,高斯式AHP分配了一个更大的面积 (26.31%) 非常高的易感性类.
- 高斯的AHP显示出优越的验证指标,包括更高的ROC AUC (0.6360对比0.6220) 和准确性.
- 基于密度的评估显示了滑坡密度的逐渐和连贯增加,在高斯AHP下增加了易感性,减少了分类模两可.
结论:
- 高斯式AHP增强了土地滑坡易感性测绘中的客观性,空间连贯性和操作可靠性.
- 该方法简化了数据预处理,减少了对专家校准的依赖,适用于数据丰富的环境.
- 建议与机器学习衍生的土地使用/土地覆盖和精细的气候数据进行整合,以进一步提高预测准确性和风险管理.
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