摘要
本文介绍了滑坡危险性预测的历史和发展,并详细讨论了基于机器学习的滑坡危险性预测模型。滑坡预测方法的历史分为定性阶段、半定量阶段、统计预测阶段和综合预测阶段四个部分。近年来,随着系统科学和非线性理论的发展,机器学习模型被广泛应用于滑坡预测中。研究表明,基于机器学习的滑坡危险性预测模型具有较高的准确率和预测效果,为进一步提高滑坡预测精度提供了参考。
This paper presents the history and development of landslide hazard prediction and discusses in detail the machine learning-based landslide hazard prediction model.The history of landslide prediction methods is divided into four parts:qualitative stage,semi-quantitative stage,statistical prediction stage and comprehensive prediction stage.In recent years,with the development of system science and nonlinear theory,machine learning models have been widely used in landslide prediction.The study shows that the landslide hazard prediction model based on machine learning has a high accuracy and prediction effect,which provides a reference for further improving the accuracy of landslide prediction.
作者
徐浩文
谭永滨
XU Haowen;TAN Yongbin(School of Surveying,Mapping and Engineering,East China University of Technology,Nanchang Jiangxi 330013,China)
出处
《北京测绘》
2023年第8期1162-1169,共8页
Beijing Surveying and Mapping
基金
自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金(MEMI-2021-2022-24,MEMI-2021-2022-31)
关键词
滑坡危险性
预测模型
非线性系统
机器学习
landslide hazard
predictive models
non-linear systems
machine learning
作者简介
徐浩文(1999—),男,浙江湖州人,硕士在读,研究方向为滑坡预测。E-mail:xhw990324@163.com;通信作者:谭永滨,E-mail:tyb@ecut.edu.cn
