摘要
综合运用InSAR和机器学习技术,对四川省金阳县进行滑坡易发性评价。通过解译滑坡更新数据集,基于12个评价因子,在Python环境下使用随机森林(RF)、支持向量机(SVM)和极端梯度提升(XGBoost)3种模型进行训练,完成滑坡易发性制图,并采用ROC曲线等验证模型预测性能。对负样本进行优化,使用机器学习得到样本优化后的滑坡易发性评价结果,并利用地表LOS向形变速率对其进行更新。结果表明,3种机器学习模型均具有较好的分区效果,XGBoost模型制图效果最佳,样本优化后模型精度最高,AUC值达到0.95。通过SBAS-InSAR技术获取地表形变速率可以减少分区错误,同时赋予滑坡易发性评价结果时效性。
We comprehensively use InSAR and machine learning technology to evaluate landslide susceptibility in Jinyang county,Sichuan province.The data set is updated by interpreting landslides.Based on 12 evaluation factors,we use three models including random forest(RF),support vector machine(SVM)and extreme gradient boosting(XGBoost)for training in Python environment to complete landslide susceptibility mapping.We use ROC curve to verify prediction performance.We optimize the negative samples,obtain the landslide susceptibility evaluation results after sample optimization using machine learning and update the landslide susceptibility results using surface LOS deformation rate.The results show that the three machine learning models have good zoning effect,and the mapping effect of XGBoost model is best.The accuracy of XGBoost model after sample optimization is highest,and the AUC value reaches 0.95.The surface deformation rate obtained by SBAS-InSAR can reduce zoning errors and give timeliness to landslide susceptibility evaluation.
作者
贾应
吴彩燕
王立娟
应欣翰
蒙齐
袁怡
廖军
马世乾
JIA Ying;WU Caiyan;WANG Lijuan;YING Xinhan;MENG Qi;YUAN Yi;LIAO Jun;MA Shiqian(School of Environment and Resource,Southwest University of Science and Technology,59 Mid-Qinglong Road,Mianyang 621010,China;Sichuan Academy of Safety Science and Technology,18 Fourth West-Wuke Road,Chengdu 610045,China;School of Civil Engineering and Architecture,Southwest University of Science and Technology,59 Mid-Qinglong Road,Mianyang 621010,China)
出处
《大地测量与地球动力学》
北大核心
2025年第3期231-238,共8页
Journal of Geodesy and Geodynamics
基金
国家自然科学基金(41301587)
重大危险源测控四川省重点实验室开放课题(KFKT-2023-01)。
