置信度加权样本与因子变异系数集成的滑坡易发性建模

Landslide susceptibility modeling based on integration of confidence-weighted samples and factor coefficient of variation

摘要

【目的】针对滑坡易发性评估中数据不确定性导致的模型精度不足问题,提出一种置信度加权样本与因子变异系数(COV)集成的建模方法。【方法】以湖南省平江县为研究区,构建三级样本置信度评价体系(1.0、0.8、0.5)对滑坡样本加权,并通过变异系数量化坡度(COV=0.15)、降雨强度(COV=0.12)等关键因子的不确定性,结合贝叶斯神经网络(BNN)和XGBoost模型开展双维度不确定性集成建模。【结果】结果表明:双维度集成方法显著提升模型性能,XGBoost模型准确率从0.721提升至0.867,AUC值从0.745升至0.892;BNN模型准确率从0.715提升至0.861,AUC值从0.738升至0.885。BNN通过概率密度函数直观展示区域滑坡概率分布的不确定性,XGBoost通过置信区间反映预测偏差。【结论】研究成果为数据稀缺、地质复杂区域的滑坡易发性评估提供了新范式,未来可通过多源数据融合和物理模型耦合进一步提升模型鲁棒性。

Abstract

[Objective] To address the problem of insufficient model accuracy caused by data uncertainty in landslide susceptibility assessment, a modeling approach is proposed that integrates confidence-weighted samples with coefficient of variation(COV) of factors. [Methods] Taking Pingjiang County, Hunan Province as the study area, a three-level sample confidence evaluation system(1. 0, 0. 8, 0. 5) was established to weight landslide samples, and COV was used to quantify the uncertainties of key factors such as slope(COV= 0. 15) and rainfall intensity(COV= 0. 12). On this basis, Bayesian neural network(BNN) and XGBoost models were employed to develop a dual-dimensional uncertainty integration model. [Results] The results showed that the proposed dual-dimensional integration method significantly improved model performance, with the accuracy of the XGBoost model increasing from 0. 721 to 0. 867, and its AUC value rising from 0. 745 to 0. 892. The BNN model achieved an accuracy improvement from 0. 715 to 0. 861, with its AUC value increasing from 0. 738 to 0. 885. BNN visually demonstrated the uncertainty of landslide probability distribution through probability density function, while XGBoost reflected prediction deviations via confidence intervals. [Conclusion] The findings provide a new paradigm for landslide susceptibility assessment in data-scarce and geologically complex regions. In the future, model robustness can be further improved through multi-source data fusion and physical model coupling.

关键词

滑坡易发性 / 数据不确定性 / 置信度加权样本 / 变异系数 / 双维度集成 / 机器学习 / 贝叶斯神经网络 / 影响因素

Key words

landslide susceptibility / data uncertainty / confidence-weighted samples / coefficient of variation / dual-dimensional integration / machine learning / Bayesian neural network / influencing factors

基金资助

湖南省自然资源科研 (标准) 后补助项目(HBZ20240112)

国家自然科学基金项目(42377453)