Sensitivity Analysis on Factors Influencing Loess Terrace Landslide Potential Using Certainty Factor Method

Mao Zhengjun; Zhang Jinge; Zhong Jiaxin; Wang Jun

doi:10.13961/j.cnki.stbctb.20230327.003

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Sensitivity Analysis on Factors Influencing Loess Terrace Landslide Potential Using Certainty Factor Method

- Sensitivity Analysis on Factors Influencing Loess Terrace Landslide Potential Using Certainty Factor Method
- Bulletin of Soiland Water Conservation Vol. 43, Issue 2, Pages: 183-192(2023)
- 作者机构：
  
  1. 西安科技大学地质与环境学院,陕西,西安,710054
  2. 西安科技大学煤炭绿色开采地质研究院,陕西,西安,710054
  3. 陕西省煤炭绿色开发地质保障重点实验室,陕西,西安,710054
  4. 宁夏回族自治区国土资源调查监测院,宁夏,银川,750002
  5. 宁夏回族自治区矿产地质调查院,宁夏,银川,750021
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.20230327.003
  CLC： P694
- Published：2023
- 稿件说明：
移动端阅览
Mao Zhengjun, Zhang Jinge, Zhong Jiaxin, et al. Sensitivity Analysis on Factors Influencing Loess Terrace Landslide Potential Using Certainty Factor Method[J]. Bulletin of Soiland Water Conservation, 2023, 43(2): 183-192.
DOI：

Mao Zhengjun, Zhang Jinge, Zhong Jiaxin, et al. Sensitivity Analysis on Factors Influencing Loess Terrace Landslide Potential Using Certainty Factor Method[J]. Bulletin of Soiland Water Conservation, 2023, 43(2): 183-192. DOI： 10.13961/j.cnki.stbctb.20230327.003.

摘要

[目的] 研究梯田型黄土滑坡隐患的影响因素，旨在揭示梯田型黄土滑坡隐患的形成机理，为防治梯田型黄土滑坡隐患提供科学依据。 [方法] 基于GEE云计算平台，以宁夏回族自治区彭阳县为研究区，使用随机森林算法，提取研究区梯田分布信息，并基于叠置分析确定了研究区梯田型黄土滑坡隐患的高、极高易发性分区，选取8个影响因素，采用确定性系数法进行了分析。 [结果] ①研究区梯田占全县总面积的47.28%，梯田影响范围内的正在变形区没有、潜在不稳定斜坡有86处。 ②梯田型黄土滑坡隐患高、极高易发性分区占研究区高、极高易发性分区总面积的46.68%，占研究区梯田总面积的27.64%。 ③降雨量、地层岩性、沟谷密度、坡向对研究区内梯田型黄土滑坡隐患的空间分布具有较强的控制作用。 [结论] 研究区梯田型黄土滑坡隐患的主要控制因素是降雨量与地层岩性，尤其是当降水量＞450 mm，且存在第四系石质黄土夹古土壤地层的地区。

Abstract

[Objective] The factors influencing landslide potential of loess terraces and the associated landslide formation mechanisms were studied in order to provide a scientific basis for the prevention and control of loess terrace landslides. [Methods] With data from the Google Earth Engine cloud computing platform for Pengyang County

Ningxia Hui Autonomous Region

we used the random forest algorithm to extract terrace distribution information in the study area. Based on the overlay analysis method

the high and extremely high susceptibility zones of potential loess terrace landslides in the study area were determined. Eight influencing factors were selected

and the certainty factor method was used for analysis. [Results] ① The terrace area in the study area accounted for 47.28% of the total area of the county. There were 86 potentially unstable slopes

and there were no deforming areas within the influence range of terraces. ② The high and extremely high susceptibility zones for potential landslides of loess terraces accounted for 46.68% of the total area of high and extremely high susceptibility zones in the study area (27.64% of the total area of terraces in the study area). ③ Rainfall

stratum lithology

gully density

and slope direction had strong controlling effects on the spatial distribution of potential landslides of loess terraces in the study area. [Conclusion] The main controlling factors for potential landslides of loess terraces in the study area were rainfall and stratum lithology

especially in the area where the annual rainfall was more than 450 mm and there were Quaternary stone loess with paleosol strata.

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references

王军,李萍,詹韵秋,等.中国耕地质量保护与提升问题研究[J].中国人口·资源与环境,2019,29(4):87-93.

自然资源部.第三次全国国土调查主要数据成果发布[EB/OL].(2021-08-26)[2022-05-14]. http://www.mnr.gov.cn/dt/ywbb/202108/t20210826_2678337.html.

新华社.第三次全国国土调查主要数据公报[EB/OL].(2021-8-26)[2022-05-14]. http://www.news.cn/2021-08/26/c_1127797077.htm.

彭俊杰.黄河流域“水—能源—粮食”纽带系统的耦合协调及时空分异[J].区域经济评论,2022(2):51-59.

蔡进军,张源润,火勇,等.宁南山区梯田土壤水分及养分特征时空变异性研究[J].干旱地区农业研究,2005,23(5):83-87.

水利部.《黄河流域水土保持公报(2020年)》发布[EB/OL].(2020-01-26)[2022-05-14]. http://www.mwr.gov.cn/xw/sjzs/202201/t20220126_1560631.html.

王爱云.1978—1985年的农村扶贫开发[J].当代中国史研究,2017,24(3):36-50,125.

岳自慧,眭克仁,刘平,等.宁南山区坡改梯作物产量变化规律研究[J].中国水土保持,2017(9):56-58,67.

刘佳丽,田佳,郑田恬,等.基于边坡稳定的黄土梯田优化设计[J].中国水土保持科学,2020,18(4):21-28.

丁智强,高璇,角媛梅,等.基于空间密度函数的哈尼梯田世界遗产地滑坡时空格局变化[J].热带地理,2020,40(3):478-486.

Camera C, Apuani T, Masetti M. Modeling the stability of terraced slopes: An approach from Valtellina (Northern Italy) [J]. Environmental Earth Sciences, 2015,74(1):855-868.

李崇巍,王志慧,汤秋鸿,等.1986—2019年黄河流域地表水体动态变化及其影响因素[J].地理学报,2022,77(5):1153-1168.

朱从谋,苑韶峰.杭州市土地利用功能时空权衡特征及影响因素分析[J].农业工程学报,2022,38(6):264-273.

靳鹏,申力,韩晓极,等.辽宁抚顺西露天矿地质灾害时空分布特征及影响因素分析[J].中国地质灾害与防治学报,2022,33(3):68-76.

Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine [J]. Mathematical Biosciences, 1975,23(3/4):351-379.

Heckerman D. Probabilistic interpretations for MYCIN’s certainty factors [M]//North-Holland: Machine Intelligence and Pattern Recognition, 1986.

向灵芝,崔鹏,张建强,等.汶川县地震诱发崩滑灾害影响因素的敏感性分析[J].四川大学学报(工程科学版),2010,42(5):105-112.

兰恒星,伍法权,周成虎,等.基于GIS的云南小江流域滑坡因子敏感性分析[J].岩石力学与工程学报,2002,21(10):1500-1506.

Lin Wei, Yin Kunlong, Wang Ningtao, et al. Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: A case study from Wuling Mountain in Hunan Province, China [J]. Natural Hazards, 2021,106(1):679-700.

覃乙根,杨根兰,谢金,等.贵州省开阳县斜坡地质灾害孕灾因子敏感性分析[J].煤田地质与勘探,2020,48(4):190-198.

Fell R, Corominas J, Bonnard C, et al. Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning [J]. Engineering Geology, 2008,102(3/4):99-111.

许强,陆会燕,李为乐,等.滑坡隐患类型与对应识别方法[J].武汉大学学报(信息科学版),2022,47(3):377-387.

Kumar L, Mutanga O. Google Earth Engine applications since inception: Usage, trends, and potentia [J]. Remote Sensing, 2018,10(10):1509.

薛薇作.数据建模与分析面向新工科高等院校大数据专业系列教材Python机器学习[M].北京：机械工业出版社,2021.

Scornet E. Random forests and kernel methods [J]. IEEE Transactions on Information Theory, 2016,62(3):1485-1500.

李万源,田佳,马琴,等.基于Google Earth Engine与机器学习的黄土梯田动态监测[J].浙江农林大学学报,2021,38(4):730-736.

汤国安,杨昕.ArcGIS地理信息系统空间分析试验教程[M].2版.北京:科学出版社,2012.

尚敏,廖芬,马锐,等.白家包滑坡变形与库水位、降雨相关性定量化分析研究[J].工程地质学报,2021,29(3):742-750.

张黎,倪万魁,张钦,等.宁夏彭阳县地质灾害[M].宁夏银川：宁夏人民出版社,2010.

Mao Zhengjun, Shi Shuojie, Li Huan, et al. Landslide susceptibility assessment using triangular fuzzy number-analytic hierarchy processing (TFN-AHP), contributing weight(CW)and random forest weighted frequency ratio (RF weighted FR)at the Pengyang county, Northwest China [J]. Environmental Earth Sciences, 2022,81:86.

田剑,汤国安,周毅,等.黄土高原沟谷密度空间分异特征研究[J].地理科学,2013,33(5):622-628.

张鹏,赵牡丹,郗家琪,等.基于梯田信息的地形湿度指数表达研究[J].水土保持通报,2018,38(1):187-191.

王恭先.滑坡学与滑坡防治技术[M].北京：中国铁道出版社,2004.

邱俊,任光明,王云南.层状反倾—顺倾边坡倾倒变形形成条件及发育规模特征[J].岩土力学,2016,37(S2):513-524,532.

毛正君,耿咪咪,毕银丽,等.紫花苜蓿—黄土复合体抗剪强度时间效应研究[J/OL].煤炭科学技术,2023-03-23,https://doi.org/10.13199/j.cnki.cst.2022-2230.

Zhang Zhongqiong, Li Miao, Wang Jia, et al. A calculation model for the spatial distribution and reserves of ground ice: A case study of the Northeast China permafrost area [J]. Engineering Geology, 2023,315:107022.

张龙飞,吴益平,苗发盛,等.推移式缓倾浅层滑坡渐进破坏力学模型与稳定性分析[J].岩土力学,2019,40(12):4767-4776.

刘虎虎,缪海波,陈志伟,等.三峡库区侏罗系顺层滑坡滑带土的剪切蠕变特性[J].岩土工程学报,2019,41(8):1573-1580.

张令非,陈忠辉,唐岳松.含弱层边坡分区滑动破坏模式及演化规律研究[J].岩石力学与工程学报,2021,40(6):1145-1154.

程圣东,杭朋磊,李鹏,等.陕南土石山区坡改梯对坡面稳定性的影响[J].水土保持研究,2018,25(5):157-161.

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