山东国土资源2025，Vol.41Issue(7)：33-38,6.DOI:10.12128/j.issn.1672-6979.2025.07.005
基于机器学习的地质灾害易发性研究
Study on the Susceptibility of Geological Hazards Based on Machine Learning
高洪军 1卞宝文 2王欣瑶2
作者信息
- 1. 日照市自然资源和规划局,山东 日照 276800
- 2. 山东省煤田地质局第一勘探队,山东青岛 266000
- 折叠
摘要
Abstract
Early identification of geological disasters and monitoring of easy—happening areas are important work in disaster prevention and reduction.In this paper,taking Pingyi county in Shandong province as the study area,the GF-1 WFV optical image,ASTER GDEM terrain data and precipitation data are fused in-to multi—source heterogeneous data.The extraction effects of three machine learning algorithms,such as TensorFlow algorithm,support vector machine,and random forest in geological hazard easy—happening areas have been compared.Geological hazard easy—happening areas in the study area from 2021 to 2024 have been extracted.By using TensorFlow algorithm,support vector machine and random forest methods,landslide easy—happening areas can all identified well.compared to other methods,TensorFlow algorithm has a higher classification accuracy with an overall accuracy of 82.33％and a Kappa coefficient of 0.82.From 2021 to 2024,the proportion of geological hazard easy—happening areas in Pingyi county ranged from 11.5％to12.5％.The fluctuations are mainly concentrated in Mengshan Dawa area in the northwest of the study area,the southern part of Tangcun reservoir,and Jiujianpeng area.The research results can pro-vide some references for the selection of extraction algorithms for geological hazard easy—happening areas and the prevention of geological hazards in Pingyi county in Shandong province.关键词
地质灾害易发性提取/机器学习/多源数据/山东平邑Key words
Geological hazard susceptibility extraction/machine learning/multi-source data/Pingyi coun-ty in Shandong province分类
天文与地球科学引用本文复制引用
高洪军,卞宝文,王欣瑶..基于机器学习的地质灾害易发性研究[J].山东国土资源,2025,41(7):33-38,6.
