基于信息量模型与随机森林模型的滑坡易发性评价——以海东市为例
Landslide Susceptibility Assessment Based on Information Volume Model and Random Forest Model—Taking Haidong City as an Example
DOI: 10.12677/sa.2025.143080, PDF,   
作者: 常锦春, 程传美, 牛鹏飞：浙江师范大学地理与环境科学学院，浙江 金华
关键词: 地质灾害；信息量模型；随机森林；易发性评价；Geological Hazards； Information Model； Random Forest； Susceptibility Assessment
摘要: 为进一步推进塔吉克斯坦地区的地质灾害评估工作，本文采用信息量模型和随机森林模型对海东市的滑坡灾害进行易发性评价，并通过ROC曲线对这两种模型的性能进行了评估。结果表明，信息量模型与随机森林模型均表现出良好的分类能力，然而随机森林模型的表现更优于信息量模型。信息量模型在对相关因子的分级分析中表现卓越，能够有效识别某因子分级内对滑坡影响最深的区间。而随机森林模型则在衡量因子的相对重要性方面具有优势。两种模型各具特色，结合使用能够更全面地掌握滑坡灾害的成因与易发性，为后续的地质灾害防治措施提供科学依据。
Abstract: To further advance the assessment of geological disasters in the Tajikistan region, this paper employs the information value model and the random forest model to evaluate the susceptibility to landslides in Haidong City, and assesses the performance of these two models using ROC curves. The results indicate that both the information value model and the random forest model demonstrate good classification capabilities; however, the random forest model outperforms the information value model. The information value model excels in the graded analysis of relevant factors, effectively identifying the intervals within a factor’s grading that have the most significant impact on landslides. In contrast, the random forest model has an advantage in measuring the relative importance of factors. Each model has its unique characteristics, and their combined use can provide a more comprehensive understanding of the causes and susceptibility of landslide disasters, offering a scientific basis for subsequent geological disaster prevention and control measures.
文章引用：常锦春, 程传美, 牛鹏飞. 基于信息量模型与随机森林模型的滑坡易发性评价——以海东市为例[J]. 统计学与应用, 2025, 14(3): 293-306. https://doi.org/10.12677/sa.2025.143080

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