基于集成Logistic回归算法的山体滑坡研究

刁君华; 冯向萍; 马新春

基于集成Logistic回归算法的山体滑坡研究

投稿时间：2024-03-18 修订日期：2024-05-27 点此下载全文

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作者	单位	邮编
刁君华	新疆农业大学	830013
冯向萍^*	新疆农业大学	830052
马新春	新疆电子研究所股份有限公司

基金项目:新疆维吾尔自治区科技支疆项目计划（2022E02080）

中文摘要:滑坡是一种常见的地质灾害，受到滑坡曲率、坡度等因素的影响。然而这些因素一般是以连续型数据存在，传统的机器学习分类算法无法适应此种数据类型，一般采用人工处理方式将其转换为离散型数据，这种方式不但耗费人力，而且无法准确划分影响因素离散化后的区间范围，产生噪声。针对于此实验提出了一种集成机器学习模型，以Logistic算法作为分类器，以随机森林算法作为连续性数据处理工具，减少人工处理方式产生的噪声影响。模型在滑坡灾害数据集上得以验证，F1值达到了90.1%。

中文关键词:机器学习人工处理噪声 Logistic 随机森林

Landslide Research Based on Integrated Logistic Regression Algorithm

Abstract:Landslide is a common geological disaster, which is affected by factors such as landslide curvature and slope. However, these factors generally exist in the form of continuous data. Traditional machine learning classification algorithms cannot adapt to this type of data. Manual processing is generally used to convert it into discrete data. This method is not only labor-intensive, but also cannot accurately classify the influencing factors. The discretized interval range generates noise. For this experiment, an integrated machine learning model was proposed, using the Logistic algorithm as the classifier and the random forest algorithm as the continuous data processing tool to reduce the noise impact caused by manual processing. The model was verified on the landslide disaster data set, and the F1 value reached 90.1%.

keywords:Machine learning manual processing noise Logistic random forest

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