摘要
Abstract
We develop a surface subsidence monitoring and prediction model for open-pit coal mines based on remote sensing technology,and construct an efficient data processing process based on satellite remote sensing Landsat and Sentinel platforms for optical and synthetic aperture radar(SAR)images,which includes radiometric correction,atmospheric correction,geometric correction,and assimilation of multi-source data.Machine learning algorithms such as Support Vector Machine(SVM),Neural Network,and Random Forest are used to predict surface subsidence by combining key features extracted from remote sensing and ground data,such as soil moisture,vegetation coverage,and terrain changes.The results show that the model can accurately predict surface subsidence in open-pit coal mines and provide powerful tools for mining area management and environmental monitoring.关键词
露天煤矿/地表沉降/地面监测/遥感技术/模型训练与验证/模型预测Key words
open-pit coal mine/land surface settlement/ground monitoring/remote sensing technology/model training and verification/model prediction分类
矿业与冶金
