不同降雨工况条件下的崩滑地质灾害危险性评价

刘帅; 朱杰勇; 杨得虎; 马博

doi:10.19509/j.cnki.dzkq.tb20220448

不同降雨工况条件下的崩滑地质灾害危险性评价

doi: 10.19509/j.cnki.dzkq.tb20220448

昆明理工大学国土资源工程学院, 昆明 650000

详细信息

作者简介:
刘帅, E-mail: 1048485867@qq.com

通讯作者:
朱杰勇, E-mail: zhujieyong@kust.edu.cn

中图分类号: P642.2
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- 文章访问数: 338
- PDF下载量: 69
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-16
- 修回日期: 2022-10-26

Geological hazard risk assessment of collapse and landslide under different rainfall conditions

Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650000, China

More Information

Author Bio:
LIU Shuai, E-mail: 1048485867@qq.com

Corresponding author: ZHU Jieyong, E-mail: zhujieyong@kust.edu.cn

摘要

摘要:
为针对性地采取预防、避让、治理等地质灾害防治与管控提供依据, 完善在地质灾害危险性评价中将降雨作为单一诱发因子参与评价体系的弊端, 在大雨、暴雨、大暴雨和特大暴雨4种不同降雨工况条件下进行了研究区崩滑地质灾害危险性评价。以云南省元阳县作为研究区域, 以栅格单元作为评价单元, 选取地貌类型、高程、坡度、坡向、曲率、工程地质岩组、土地利用类型、断层距离和河流距离9个评价因子, 采用主观的层次分析法与客观的信息量模型相结合的加权信息量模型对元阳县崩塌、滑坡进行了地质灾害易发性评价。研究结果表明: 基于自然间断点法元阳县域可分为低、中、高、极高4类易发区, 4类易发区面积分别占元阳县面积的21.55%, 35.46%, 25.53%和17.16%。利用ROC曲线得出区划结果精度AUC值为0.812, 表明区划结果很好。在易发性评价基础上, 以年平均最大日降雨量为诱发因素, 分别对大雨、暴雨、大暴雨和特大暴雨4种工况条件下的研究区进行了崩塌、滑坡地质灾害危险性评价, 得到了大雨([25, 50) mm)工况、暴雨([50, 100) mm)工况、大暴雨([100, 250]mm)工况和特大暴雨(>250 mm)工况4种条件下基于极值假设的研究区崩滑地质灾害危险性评价结果, 并对不同降雨工况条件下的地质灾害危险性评价结果进行了对比分析, 确定了地质灾害危险性评价结果在不同降雨条件下的空间合理性。通过与实际调查结果的对比表明, 4种不同降雨工况条件下的崩滑地质灾害危险性评价结果与实际高度吻合, 说明评价结果具有较高的可靠性与合理性。
- 栅格单元 /
- 加权信息量模型 /
- 地质灾害易发性评价 /
- 地质灾害危险性评价 /
- ROC曲线 /
- 降雨工况 /
- 崩滑
Abstract: Objective
This study provides a basis for the prevention, avoidance, management and control of geological hazards. The disadvantage of considering rainfall as a single inducing factor in the evaluation system of geological hazard risk should be overcome. In this paper, risk assessments of collapse and landslide geological hazards in the study area were carried out under four different rainfall conditions: heavy rain, rainstorms, heavy rainstorms and extraordinary rainstorms.
Methods
Yuanyang County, Yunnan Province, was selected as the study area, and the grid unit was chosen as the evaluation unit. Nine evaluation factors were selected, namely, landform type, elevation, slope, aspect, curvature, engineering geological rock group, land use type, distance from fault and distance from river, respectively. A weighted information model combining a subjective analytic hierarchy process and an objective information model was used to evaluate susceptibility to collapse and landslides in Yuanyang County.
Results
According to the natural discontinuity point method, Yuanyang County is divided into four types, low, medium, high and extremely high susceptibility areas, accounting for 21.55%, 35.46%, 25.53% and 17.16% of the area of Yuanyang County, respectively. According to the ROC curve, the accuracy of the zoning results was 0.812, and the zoning results were good. Based on the susceptibility assessment, taking the annual average maximum daily rainfall as the inducing factor, risk assessments of collapse and landslide geological hazards in the study area were carried out under four working conditions-heavy rain, rainstorm, heavy rainstorm and extraordinary rainstorm, and results for collapse and landslide geological hazards under four conditions: heavy rain ([25, 50) mm) and rainstorms([50, 100) mm), heavy rainstorm ([100, 250] mm) and extraordinary rainstorm (>250 mm) data were thus obtained. The results of geological hazard risk assessments under different rainfall conditions are compared and analysed, and the spatial rationality of geological hazard risk assessment results under different rainfall conditions is determined.
Conclusion
The collapse and landslide hazard evaluation results under different rainfall conditions are highly consistent with the actual survey results, confirming the high reliability and rationality of the evaluation results.
- grid unit /
- weighted information model /
- susceptibility assessment of geological disaster /
- risk assessment of geological hazard /
- ROC curve /
- rainfall condition /
- collapse and landslide
注释:

所有作者声明不存在利益冲突。

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图 1 元阳县灾害点分布图

Q.第四系河漫滩，阶地堆积物、残破积物砾石、砂、粉砂、粉砂质黏土层；N₁.新近系角砾状灰岩、砾岩夹泥岩、泥质粉砂岩，局部夹煤线及石膏层；E₁.古近系沙砾岩、泥岩，夹砂岩及石膏层，下层夹多层白云岩；K₁m.下白垩统曼岗组石英砂岩、粉砂岩、粉砂质泥岩、中下部夹砾岩，底部为砾岩；T₃λπ.上三叠统高山寨组流纹斑岩; T₃g.上三叠统高山寨组页岩、砂岩、砂砾岩夹流纹斑岩、英安斑岩；T₃n.上三叠统鸟格组页岩夹灰岩、白云岩；T₂n.中三叠统牛上组板岩夹页岩、砂岩；T₂g^e.中三叠统个旧组灰岩、灰质白云岩；T₁x.下三叠统洗马塘组泥岩、砂岩、粉砂岩；P₁m.下二叠统茅口组灰色白中至巨厚层状灰夹白云岩、白云质灰岩；P₁q.下二叠统栖霞组灰色厚层状态灰岩、夹白云质灰岩，局部夹粉砂岩薄层；P₁n.下二叠统中酸性喷出岩；Pβ.二叠纪玄武岩；C₃.上石炭统灰白色生物碎屑灰岩；C₂.中石炭统浅灰色中厚至块状生物碎屑灰岩夹灰岩；C₁.下石炭统灰至浅灰块状生物碎屑灰岩夹薄层条带状灰岩；D₁l.中泥盆统老寨组浅灰色中厚层至块状灰岩，上部夹钙质页岩；D₂s.中泥盆统宋家寨组泥质、硅质页岩夹灰岩；D₂m.中泥盆统马鹿洞组浅灰色灰岩、白云质灰岩，夹泥质灰岩、角砾状灰岩；S₂.中志留统浅灰色灰岩、白云岩，底部夹页岩、砂岩；S₁.下志留统粉砂岩、石英砂岩夹板岩；O₁b^c.下奥陶统白马寨组上段石英砂岩、长石砂岩夹页岩；O₁b^b.下奥陶统白马寨组中段中-粗粒砂岩、砾岩、板岩；O₁b^a.下奥陶统白马寨组下段板岩、细砂岩、中砂岩。哀牢山群：Ptw.乌都坑组片麻岩夹斜长角闪岩与少量麻粒岩；Pt f.风港组片麻岩、变粒岩、黑云透辉岩；Pta^a.阿龙组角闪斜长片麻岩与片麻岩互层，夹变粒岩、透辉岩与石墨片岩；Pta^b.阿龙组大理岩与片麻岩、角闪岩、变粒岩互层；Ptx^b.小羊街组片麻岩与变粒岩互层。喜山期燕山期：ξx₆.云辉玢岩；ξo₆.角闪石英正长岩、黑云辉石石英正长岩；γ₅^2-3.花岗岩；βμ₃^2-3.辉绿岩、辉绿玢岩。印支期：γπ₅¹.花岗斑岩；γ₅¹.花岗岩。华力西期：σ₄a.变闪长岩；ηγ₅³.黑云二长花岗岩；γπ.花岗斑岩；ν.基性侵入岩、基性-超基性侵入岩

Figure 1. Distribution of collapse and landslide disaster sites in Yuanyang County

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图 2 卜拉寨滑坡全貌

Figure 2. Panorama of the Bulazhai landslide

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图 3 卜拉寨滑坡工程地质剖面示意图

Figure 3. Schematic diagram of engineering geological section for the Bulazhai landslide

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图 4 评价因子分级图

Figure 4. Evaluation factor grading chart

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图 5 评价因子特征重要性

Figure 5. Significance of evaluation factor features

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图 6 崩滑地质灾害易发性区划图

Figure 6. Zoning map of susceptible slide geological hazard

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图 7 ROC曲线

Figure 7. ROC curve

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图 8 不同降雨工况条件下的地质灾害危险性区划图

Figure 8. Zoning map of geological hazard under different rainfall conditions

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图 9 不同降雨工况下地质灾害危险性分区所占面积

Figure 9. Areas occupied by geological hazard zones under different rainfall conditions

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图 10 不同降雨工况下地质灾害危险性分区所包含灾害点数

Figure 10. Disaster points included in geological disaster risk zones under different rainfall conditions

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图 11 危险性区划结果与实际对比

a.所占面积对比；b.所包含灾害点对比

Figure 11. Comparison of risk zoning results with the actual situation

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表 1 评价因子权重及信息量

Table 1. Weight and information scales of the evaluation factors

评价因子	二级因子	S_i/个	S_i/S	N_i/个	N_i/N	I	w_j/%	CR
地貌类型	堆积河谷地貌	238 887	0.07	22	0.09	0.25	3.91
	岩溶中山地貌	238 887	0.07	24	0.10	0.33
	构造侵蚀中山地貌	2 873 413	0.84	199	0.81	-0.04
	构造侵蚀低山地貌	53 971	0.02	0	0.00	0.00
土地利用类型	林地	2 601 414	0.73	120	0.49	-0.41	6.80
	灌木	2 993	0.00	0	0.00	0.00
	草地	580 768	0.16	19	0.08	-0.75
	耕地	232 178	0.07	7	0.03	-0.83
	建筑	30 571	0.01	44	0.18	3.04
	裸地或稀疏植被	78 435	0.02	55	0.22	2.32
	开阔水域	14 991	0.00	0	0.00	0.00
	草本湿地	61	0.00	0	0.00	0.00
断层距离/m	50	63 229	0.02	4	0.02	-0.09	13.14
	100	63 331	0.02	5	0.02	0.13
	300	246 527	0.07	28	0.11	0.50
	500	235 754	0.07	23	0.09	0.34
	1 000	541 180	0.15	38	0.16	0.01
	3 000	1 281 573	0.36	91	0.37	0.03
	>3 000	1 109 778	0.31	56	0.23	-0.32
高程/m	[110, 650)	408 649	0.12	21	0.09	-0.30	16.34
	[650, 1 050)	729 924	0.21	23	0.09	-0.79
	[1 050, 1 400)	846 548	0.24	52	0.21	-0.12
	[1 400, 1 700)	811 256	0.23	118	0.48	0.74
	[1 700, 2 150)	545 512	0.15	30	0.12	-0.23
	[2 150, 2 950]	199 476	0.06	1	0.00	-2.62
工程地质岩组	薄-中层状较软泥岩、泥质粉砂岩岩组	142 964	0.04	3	0.01	-1.19	8.88	0.046 2
	中-厚层状坚硬强岩溶化灰岩、白云岩、白云质灰岩组	55 908	0.02	1	0.00	-1.35
	黏土、砂质黏土夹砾石多层土体	14 812	0.00	1	0.00	-0.02
	薄-中层状坚硬片麻岩、变粒岩岩组	1 658 434	0.47	117	0.48	0.02
	薄-中层状坚硬大理岩、角闪岩、变粒岩岩组	131 907	0.04	4	0.02	-0.82
	薄-中层状较硬泥页岩、粉砂岩岩组	541 466	0.15	49	0.20	0.27
	块状坚硬侵入岩岩组	441 545	0.12	38	0.16	0.22
	碎裂状、块状较坚硬喷出岩岩组	196 231	0.06	7	0.03	-0.66
	中-厚层状坚硬弱岩溶化灰岩、白云岩夹板岩岩组	303 885	0.09	23	0.09	0.09
	中-厚层状坚硬砂岩、石英砂岩岩组	7 250	0.00	2	0.01	1.38
	中-厚层状坚硬中岩溶化灰岩、白云质灰岩岩组	29 680	0.01	0	0.00	0.00
	薄层状较硬板岩岩组	16 893	0.00	0	0.00	0.00
坡度/(°)	[0, 8)	131 433	0.04	7	0.03	-0.27	16.94
	[8, 15)	450 167	0.13	51	0.21	0.49
	[15, 25)	1 235 738	0.35	103	0.42	0.18
	[25, 35)	1 161 406	0.33	64	0.26	-0.23
	[35, 90]	550 193	0.16	20	0.08	-0.65
坡向	平面	28 351	0.01	1	0.00	-0.68	8.70
	北	613 970	0.17	39	0.08	-0.14
	东北	534 362	0.15	37	0.15	0.00
	东	405 228	0.11	31	0.13	0.10
	东南	449 606	0.13	29	0.12	-0.07
	南	408 220	0.12	38	0.16	0.30
	西南	335 603	0.09	21	0.09	-0.10
	西	317 065	0.09	22	0.09	0.00
	西北	442 634	0.13	27	0.11	-0.13
曲率	凹型坡	934 362	0.26	49	0.20	-0.28	3.04
	平面坡	1 528 094	0.43	121	0.49	0.13
	凸型坡	1 069 874	0.30	75	0.31	0.01
河流距离/m	50	419 847	0.12	33	0.13	0.13	5.32
	100	376 932	0.11	25	0.10	-0.04
	300	1 189 564	0.34	74	0.30	-0.11
	500	746 745	0.21	55	0.22	0.06
	1 000	698 574	0.20	55	0.22	0.13
	3 000	109 676	0.03	3	0.01	-0.93
注：S_i为含有二级因子x_i的栅格单元数；S为评价栅格单元总数；N_i为二级因子x_i内灾害点数；N为灾害点总数；I为信息量值；w_j为第j个评价因子的权重；CR为随机一致比

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表 2 易发性分级数据统计

Table 2. Risk classification of statistical data

易发性等级	灾害点数	占比/%	区间面积/km²	占比/%
低	31	11.97	475.55	21.55
中	61	23.55	782.70	35.46
高	67	25.87	556.89	25.23
极高	100	38.61	392.01	17.16

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表 3 不同降雨工况条件下的地质灾害危险性区划结果对比

Table 3. Comparison of geological hazard zoning results under different rainfall conditions

工况	危险性	面积/km²	占比/%	崩滑点数/处	占比/%
大雨工况	低	285.47	12.88	3	1.32
	中	897.58	40.51	38	16.67
	高	796.35	35.94	83	36.40
	极高	236.47	10.67	104	45.61
暴雨工况	低	211.76	9.56	2	0.88
	中	785.26	35.44	35	15.35
	高	923.09	41.66	80	35.09
	极高	295.77	13.35	111	48.68
大暴雨工况	低	117.02	5.28	2	0.88
	中	640.59	28.91	25	10.96
	高	1 077.75	48.64	76	33.33
	极高	380.52	17.17	125	54.82
特大暴雨工况	低	46.52	2.10	2	0.88
	中	391.71	17.68	6	2.63
	高	1 300.44	58.69	79	34.65
	极高	477.21	21.54	141	61.84

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表 4 危险性区划结果与实际对比

Table 4. Risk zoning results versus the ones in actual situation

区划结果来源	危险性	面积/km²	面积占比/%	崩滑点数/处	崩滑点占比/%
本研究大暴雨工况区划结果	低	117.02	5.28	2	0.88
	中	640.59	28.91	25	10.96
	高	1077.75	48.64	76	33.33
	极高	380.52	17.17	125	54.82
研究区详细调查区划结果	低	202.77	9.15	0	0.00
	中	603.41	27.23	17	7.46
	高	1035.42	46.73	81	35.53
	极高	374.28	16.89	130	57.02

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参考文献(40)

[1]	张茂省, 唐亚明. 地质灾害风险调查的方法与实践[J]. 地质通报, 2008, 27(8): 1205-1216. https://www.cnki.com.cn/Article/CJFDTOTAL-ZQYD200808021.htm ZHANG M S, TANG Y M. Method and practice of geological hazard risk investigation[J]. Geological Bulletin of China, 2008, 27(8): 1205-1216. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZQYD200808021.htm
[2]	齐信, 唐川, 陈州丰, 等. 地质灾害风险评价研究[J]. 自然灾害学报, 2012, 21(5): 33-40. QI X, TANG C, CHEN Z F, et al. Research on risk assessment of geological hazards[J]. Journal of Natural Disasters, 2012, 21(5): 33-40. (in Chinese with English abstract)
[3]	李益敏, 李驭豪, 赵志芳. 基于确定性系数模型的泸水市泥石流易发性评价[J]. 水土保持研究, 2019, 26(4): 336-342. https://www.cnki.com.cn/Article/CJFDTOTAL-STBY201904052.htm LI Y M, LI Y H, ZHAO Z F. Evaluation of debris flow susceptibility in Lushui City based on deterministic coefficient model[J]. Soil and Water Conservation Research, 2019, 26(4): 336-342. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-STBY201904052.htm
[4]	胡燕, 李德营, 孟颂颂, 等. 基于证据权法的巴东县城滑坡灾害易发性评价[J]. 地质科技通报, 2020, 39(3): 187-194. doi: 10.19509/j.cnki.dzkq.2020.0320 HU Y, LI D Y, MENG S S, et al. Evaluation of landslide susceptibility in Badong County based on the weight of evidence method[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 187-194. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2020.0320
[5]	王磊, 常鸣, 邢月龙. 基于信息量法模型与GIS的滑坡地质灾害风险性评价[J]. 地质灾害与环境保护, 2021, 32(2): 14-20. https://www.cnki.com.cn/Article/CJFDTOTAL-DZHB202102003.htm WANG L, CHANG M, XING Y L. Risk assessment of landslide geological hazards based on information method model and GIS[J]. Geological Hazards and Environmental Protection, 2021, 32(2): 14-20. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DZHB202102003.htm
[6]	牛瑞卿, 彭令, 叶润青, 等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报(地球科学版), 2012, 42(2): 430-439. https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201202019.htm NIU R Q, PENG L, YE R Q, et al. Landslide susceptibility evaluation based on rough set support vector machine[J]. Journal of Jjilin University(Earth Science Edition), 2012, 42(2): 430-439. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201202019.htm
[7]	郑迎凯, 陈建国, 王成彬, 等. 确定性系数与随机森林模型在云南芒市滑坡易发性评价中的应用[J]. 地质科技通报, 2020, 39(6): 131-144. doi: 10.19509/j.cnki.dzkq.2020.0616 ZHENG Y K, CHEN J G, WANG C B, et al. Application of deterministic coefficient and random forest model to landslide susceptibility assessment in Mangshi, Yunnan Province[J]. Bulletin of Geological Science and Technology, 2020, 39(6): 131-144. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2020.0616
[8]	熊小辉, 汪长林, 白永健, 等. 基于不同耦合模型的县域滑坡易发性评价对比分析: 以四川普格县为例[J]. 中国地质灾害与防治学报, 2022, 33(4): 114-124. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202204014.htm XIONG X H, WANG C L, BAI Y J, et al. Comparative analysis of county landslide susceptibility assessment based on different coupling models: Taking Puge County, Sichuan Province as an example[J]. Chinese Journal of Geological Disasters and Prevention, 2022, 33(4): 114-124. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202204014.htm
[9]	周苏华, 付宇航, 徐智文, 等. 基于主客观赋权法的福建省地质灾害易发性评价[J]. 安全与环境学报, 2023, 23(9): 3204-3214. https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202309028.htm ZHOU S H, FU Y H, XU Z W, et al, Evaluation of geological hazard susceptibility in Fujian Province based on subjective and objective weighting method[J]. Journal of Safety and Environment, 2023, 23(9): 3204-3214. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202309028.htm
[10]	李永平, 赵晓燕, 罗杨洁, 等. 基于加权耦合模型的矿山地质环境影响性评价: 以攀枝花市为例[J]. 内江师范学院学报, 2022, 37(6): 59-65. https://www.cnki.com.cn/Article/CJFDTOTAL-NJSG202206010.htm LI Y P, ZHAO X Y, LUO Y J, et al. Evaluation of mine geological environment impact based on weighted coupling model: Taking Panzhihua City as an example[J]. Journal of Neijiang Normal University, 2022, 37(6): 59-65. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-NJSG202206010.htm
[11]	王晓形. 基于AHP及专家打分法的大跨度隧道风险评估[J]. 现代隧道技术, 2020, 57(增刊1): 233-240. https://www.cnki.com.cn/Article/CJFDTOTAL-XDSD2020S1029.htm WANG X X. Risk assessment of large-span tunnel based on APH and expert scoring method[J]. Modern Tunnel Technology, 2020, 57(S1): 233-240. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-XDSD2020S1029.htm
[12]	管斌. 基于GIS技术和模糊综合评判法的铜陵市地质灾害易发性评价研究[D]. 合肥: 合肥工业大学, 2020. GUAN B. Evaluation of geological disaster susceptibility in Tongling City based on GIS technology and fuzzy comprehensive evaluation method[D]. Hefei: Hefei University of Technology, 2020. (in Chinese with English abstract)
[13]	魏江波, 赵洲. 基于加权确定性系数法的地质灾害易发性分析[J]. 煤田地质与勘探, 2018, 46(6): 108-114. https://www.cnki.com.cn/Article/CJFDTOTAL-MDKT201806015.htm WEI J B, ZHAO Z. Geological disaster susceptibility analysis based on weighted certainty factor method[J]. Coal Geology and Exploration, 2018, 46(6): 108-114. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-MDKT201806015.htm
[14]	任敬, 范宣梅, 赵程, 等. 贵州省都匀市滑坡易发性评价研究[J]. 水文地质工程地质, 2018, 45(5): 165-172. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201805024.htm REN J, FAN X M, ZHAO C, et al. Evaluation of landslide susceptibility in Duyun City, Guizhou Province[J]. Hydrogeology and Engineering Geology, 2018, 45(5): 165-172. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201805024.htm
[15]	陈飞, 蔡超, 李小双, 等. 基于信息量与神经网络模型的滑坡易发性评价[J]. 岩石力学与工程学报, 2020, 39(增刊1): 2859-2870. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2020S1027.htm CHEN F, CAI C, LI X S, et al. Evaluation of landslide susceptibility based on information quantity and neural network model[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(S1): 2859-2870. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2020S1027.htm
[16]	MALKA A. Landslide susceptibility mapping of Gdynia using geographic information system-based statistical models[J]. Natural Hazards, 2021, 107(1): 639-674. doi: 10.1007/s11069-021-04599-8
[17]	ZENG B, CHEN X. Assessment of shallow landslide susceptibility using an artificial neural network[J]. Arabian Journal of Geosciences, 2021, 14(6): 1-18.
[18]	易靖松, 王峰, 程英建, 等. 高山峡谷区地质灾害危险性评价: 以四川省阿坝县为例[J]. 中国地质灾害与防治学报, 2022, 33(3): 134-142. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202203015.htm YI J S, WANG F, CHENG Y J, et al. Risk assessment of geological disasters in alpine canyon areas: Taking Aba County, Sichuan Province as an example[J]. Chinese Journal of Geological Disasters and Prevention, 2022, 33(3): 134-142. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202203015.htm
[19]	李萍, 李丽慧, 刘昊磾, 等. 澜沧江流域重大水电工程扰动灾害风险评价[J]. 工程地质学报, 2022, 30(3): 635-647. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202203004.htm LI P, LI L H, LIU H D, et al. Risk assessment of disturbance disaster for major hydropower projects in Lancang River Basin[J]. Journal of Engineering Geology, 2022, 30(3): 635-647. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202203004.htm
[20]	姬永涛, 王鲜, 郝业, 等. 基于斜坡单元的陕西省城镇地质灾害风险调查评价: 以西安市蒋村街道为例[J]. 灾害学, 2022, 37(4): 211-219. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHXU202204032.htm JI Y T, WANG X, HAO Y, et al. Risk investigation and evaluation of urban geological disasters in Shaanxi Province based on slope unit: Taking Jiangcun Street in Xi 'an as an example[J]. Disasterology, 2022, 37(4): 211-219. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZHXU202204032.htm
[21]	陆跃萍. 从元阳县城滑坡灾害看山区城镇建设的环境因素[J]. 云南环境科学, 1993, 12(1): 39-41. https://www.cnki.com.cn/Article/CJFDTOTAL-YNHK199301013.htm LU Y P. Environmental factors of urban construction in mountainous area from landslide disaster of Yuanyang County[J]. Yunnan Environmental Science, 1993, 12(1): 39-41. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-YNHK199301013.htm
[22]	戚琦. 基于GIS和目标层次联合分析方法的元阳县地质灾害易发程度评价研究[D]. 北京: 中国地质大学(北京), 2012. QI Q. Evaluation of susceptibility degree of geological disasters in Yuanyang County based on GIS and object-level joint analysis method[D]. Beijing: China University of Geosciences(Beijing), 2012. (in Chinese with English abstract)
[23]	于成龙. 和龙市典型地质灾害风险性区划与地质环境承载力综合评价研究[D]. 长春: 吉林大学, 2021. YU C L. Study on risk regionalization of typical geological disasters and comprehensive evaluation of geological environment carrying capacity in Helong City[D]. Changchun: Jilin University, 2021. (in Chinese with English abstract)
[24]	朱文慧, 晏鄂川, 邹浩, 等. 湖北省黄冈市降雨型滑坡气象预警判据[J]. 地质科技通报, 2022, 41(6): 45-53. doi: 10.19509/j.cnki.dzkq.2022.0132 ZHU W H, YAN E C, ZOU H, et al. Meteorological warning criterion of rainfall-induced landslide in Huanggang City, Hubei Province[J]. Bulletin of Geological Science and Technology, 2022, 41(6): 45-53. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0132
[25]	殷坤龙, 张宇, 汪洋. 水库滑坡涌浪风险研究现状和灾害链风险管控实践[J]. 地质科技通报, 2022, 41(2): 1-12. doi: 10.19509/j.cnki.dzkq.2022.0064 YIN K L, ZHANG Y, WANG Y. Reservoir landslide surge risk research status and disaster chain risk control practice[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 1-12. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0064
[26]	YUAN X, LIU C, NIE R, et al. Comparative analysis of certainty factor-based machine learning methods for collapse and landslide susceptibility mapping in Wenchuan County, China[J]. Remote Sens., 2022, 14(14): 3259. doi: 10.3390/rs14143259
[27]	JIN B, YIN K, LI Q, et al. Susceptibility analysis of land subsidence along the transmission line in the Salt Lake area based on remote sensing interpretation[J]. Remote Sens., 2022, 14(13): 3229. doi: 10.3390/rs14133229
[28]	XU L, CHEN C, QING, F, et al. Graph-represented broad learning system for landslide susceptibility mapping in Alpine-Canyon region[J]. Remote Sens., 2022, 14(12): 2773. doi: 10.3390/rs14122773
[29]	CHANG L, ZHANG R, WANG C, et al. Evaluation and prediction of landslide susceptibility in Yichang section of Yangtze River Basin based on integrated deep learning algorithm[J]. Remote Sens., 2022, 14(11): 2717. doi: 10.3390/rs14112717
[30]	申泽西, 张强, 吴文欢, 等. 青藏高原及横断山区地质灾害易发区空间格局及驱动因子[J]. 地理学报, 2022, 77(5): 1211-1224. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXB202205007.htm SHEN Z X, ZHANG Q, WU W H, et al. Spatial pattern and driving factors of geological disaster-prone areas in Qinghai-Tibet Plateau and Hengduan Mountains[J]. Geography, 2022, 77(5): 1211-1224. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DLXB202205007.htm
[31]	毛启曦, 胡元平, 刘万亮, 等. 基于信息量模型的利川市谋道-建南线性廊道区斜坡地质灾害易发性评价[J]. 资源环境与工程, 2022, 36(2): 175-180. https://www.cnki.com.cn/Article/CJFDTOTAL-HBDK202202006.htm MAO Q X, HU Y P, LIU W L, et al. Susceptibility assessment of slope geological disasters in Lichuan Mudao-Jiannan linear corridor area based on information quantity model[J]. Resources Environment & Engineering, 2022, 36(2): 175-180. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-HBDK202202006.htm
[32]	张伟. 浅析广东省崩滑流地质灾害与地层岩性关系[J]. 西部资源, 2017(4): 104-105. https://www.cnki.com.cn/Article/CJFDTOTAL-XBZY201704044.htm ZHANG W. Analysis of the relationship between geological disasters and stratigraphic lithology in Guangdong Province[J]. Western Resources, 2017(4): 104-105. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-XBZY201704044.htm
[33]	刘艳辉, 黄俊宝, 肖锐铧, 方然可. 基于随机森林的福建省区域滑坡灾害预警模型研究[J]. 工程地质学报, 2022, 30(3): 944-955. LIU Y H, HUANG J B, XIAO R H, et al. Research on early warning model of regional landslide disaster in Fujian Province based on random forest[J]. Journal of Engineering Geology, 2022, 30(3): 944-955. (in Chinese with English abstract)
[34]	方匡南, 吴见彬, 朱建平, 等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011, 26(3): 32-38. https://www.cnki.com.cn/Article/CJFDTOTAL-TJLT201103007.htm FANG K N, WU J B, ZHU J P, et al. Review of random forest methods[J]. Forum on Statistics and Information, 2011, 26(3): 32-38. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-TJLT201103007.htm
[35]	周金成, 李俊奇, 杨超. 区域特征参数及降雨等级差异性对合流制系统溢流影响模拟研究[J]. 给水排水, 2021, 57(9): 44-52. https://www.cnki.com.cn/Article/CJFDTOTAL-JZJS202109011.htm ZHOU J C, LI J Q, YANG C. Simulation study on the influence of regional characteristic parameters and rainfall grade difference on the overflow of combined system[J]. Water Supply and Drainage, 2021, 57(9): 44-52. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-JZJS202109011.htm
[36]	姚博. 不同降雨等级及林分间伐强度对红松人工林穿透雨特征的影响[J]. 防护林科技, 2019(7): 5-8. https://www.cnki.com.cn/Article/CJFDTOTAL-FHLK201907002.htm YAO B. Effects of different rainfall levels and stand thinning intensity on throughfall characteristics of Pinus koraiensis plantation[J]. Shelterbelt Technology, 2019(7): 5-8. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-FHLK201907002.htm
[37]	张泽铭, 张翠翠, 侯红运, 等. 《降水量等级》(GB/T 28592-2012)应用实例[J]. 现代农业科技, 2016(19): 223-226. https://www.cnki.com.cn/Article/CJFDTOTAL-ANHE201619125.htm ZHANG Z M, ZHANG C C, HOU H Y, et al. Precipitation Grade(GB/T28592-2012) application example[J]. Modern Agricultural Science and Technology, 2016(19): 223-226. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ANHE201619125.htm
[38]	张继权, 荣广智, 李天涛, 等. 多致灾因子诱发地质灾害链综合风险评价技术[J]. 中国减灾, 2022(7): 23-26. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJI202207006.htm ZHANG J Q, RONG G Z, LI T T, et al. Comprehensive risk assessment technology of geological disaster chain induced by multiple disaster-causing factors[J]. China Disaster Reduction, 2022(7): 23-26. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJI202207006.htm
[39]	管家琳, 黄炎和, 林金石, 等. 基于信息量模型与随机森林模型的崩岗风险对比评估[J]. 山地学报, 2021, 39(4): 539-551. https://www.cnki.com.cn/Article/CJFDTOTAL-SDYA202104008.htm GUAN J L, HUANG Y H, LIN J S, et al. Comparative assessment of slope collapse risk based on information information model and random forest model[J]. Journal of Mountain Science, 2021, 39(4): 539-551. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-SDYA202104008.htm
[40]	毕鸿基, 聂磊, 曾超, 等. 基于三种多变量不安定指数分析模型的汶川县地质灾害易发性评价[J]. 中国地质灾害与防治学报, 2022, 33(1): 123-131. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202201015.htm BI H J, NIE L, ZENG C, et al. Evaluation of geological hazard susceptibility in Wenchuan County based on three multivariate instability index analysis models[J]. Chinese Journal of Geological Hazards and Prevention, 2022, 33(1): 123-131. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202201015.htm