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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms

delete2025-05-26
delete0
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OA
AI
X
Xia Zhao *
陈
陈卫 (Wei Chen) *
P
Paraskevas Tsangaratos
I
Ioanna Ilia
Q
Qingfeng He *
DOI：10.1080/10106049.2025.2507919delete
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摘要

摘要

En 中文
地质环境驱动的滑坡易发性模型优化：山区灾害减灾的关键挑战。中国小金县地质复杂，灾害活跃，对人类和经济安全构成威胁。本研究通过比较三种数据驱动模型——WoE、优化RF和RBFNetworks，对小金县地形异质性高的滑坡易发性进行了评估。分析了12个条件因子与滑坡的时空相关性，确定了因子间的多重共线性，并通过MDA和MDG量化了因子的重要性，特别是高程、土壤和距道路距离。WoE模型表现出卓越性能（训练集AUC：0.899，验证集AUC：0.892），优于RF（训练集AUC：0.880，验证集AUC：0.874）和RBFNetwork（训练集AUC：0.866，验证集AUC：0.863）。研究结果对小金县的土地开发和管理具有重要意义，同时也挑战了机器学习范式。
Keyword:
Landslide susceptibility modeling
weights-of-evidence
random forest
radial basis function network
GIS

期刊

Geocarto International 封面图
Geocarto International
IF:
3.5
论文数:
2.4K
被引数:
6.9K

机构

N
National Technical University of Athens
学者数:
9.7K
论文数: 9.5K
被引数: 8.2K
引用论文

引用论文

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