[1] 李昌文,王宝成,冯帅.西北黄土湿陷性地区管道地质灾害类型分析[J].中国建材科技,2019,28(1):120-124. [2] 高懿琼,骆正山,毕傲睿,等.湿陷性黄土区长输管道耦合协调危险性评价[J].灾害学,2020,35(4):20-23. [3] 庄红峰.隧道窑热工设备管理注意事项[J].砖瓦世界,2019(3):18-23. [4] 陈家乐,倪万魁,王海曼,等.原状黄土土—水特征曲线与湿陷性的相关性[J].中国地质灾害与防治学报,2024,35(2):107-114. [5] 孙伟,刘峻峰,高海英,等.输油管道重点区段SBAS-InSAR地质灾害监测研究[J].石油与天然气化工,2021,50(4):140-146. [6] 冉林,马鹏辉 彭建兵,孔嘉旭,等.甘肃黑方台“10·5”黄土滑坡启动及运动特征分析[J].中国地质灾害与防治学报,2022,33(6):1-9. [7] Lombardi L,Nocentini M,Frodella W,et al.The Calatabiano landslide (southern Italy):preliminary GB-InSAR monitoring data and remote 3D mapping[J].Landslides,2017,14(2):685-696. [8] 赵超英,刘晓杰,张勤,等.甘肃黑方台黄土滑坡InSAR识别、监测与失稳模式研究[J].武汉大学学报(信息科学版),2019,44(7):996-1007. [9] 张磊.油气管道沿线地质灾害识别与评价方法研究[D].成都:四川师范大学,2020. [10] 池金洺,于洋,冯娟龙,等.基于RUSLE模型的妫水河流域土壤侵蚀时空变化特征[J].水土保持学报,2024,38(1):70-78. [11] 张领旗,谢酬,陈蜜,等.时序InSAR技术在三峡库区特高压输电通道滑坡隐患识别的应用[J].测绘通报,2023(2):97-103. [12] Ferretti A,Fumagalli A,Novali F,et al.A new algorithm for processing interferometric data-stacks:SqueeSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(9):3460-3470. [13] Ansari H,De Zan F,Bamler R.Efficient phase estimation for interferogram stacks[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(7):4109-4125. [14] Wischmeier W H,Smith D D.Predicting rainfall-erosion losses from cropland east of the Rocky mountains:guide for selection of practices for soil and water conservation[M].Washington,DC:Agricultural Research Service,U.S.Dept.of Agriculture in cooperation with Purdue Agricultural Experiment Station,1965. [15] 蔡崇法,丁树文,史志华,等.应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究[J].水土保持学报,2000,14(2):19-24. [16] McCool D K,Brown L C,Foster G R,et al.Revised slope steepness factor for the universal soil loss equation[J].Transactions of the ASAE,1987,30(5):1387-1396. [17] Wischmeier W H,Smith D D.Predicting rainfall erosion losses:a guide to conservation planning[M].Washington DC:US Department of Agriculture,Science and Education Administration,1978. [18] 王丽园,赵体侠,朱文博,等.基于RUSLE模型的秦岭—大巴山地土壤侵蚀时空特征分析[J].水土保持学报,2024,38(1):113-121. [19] 何子淼.黄土高原典型坡面土壤侵蚀因子阈值研究[D].北京:北京林业大学,2021. [20] Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32. [21] Alatefi S,Almeshal A M.A new model for estimation of bubble point pressure using a Bayesian optimized least square gradient boosting ensemble[J].Energies,2021,14(9):2653. [22] Lundberg S M,Lee S I.A unified approach to interpreting model predictions[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.[S.l.]:ACM Press,2017:4768-4777. |