基于AHP-信息量模型的桂林市地质灾害危险性评价

doi:10.16088/j.issn.1001-6600.2024082301

Abstract

Abstract: Guilin City, with its many mountains and hills, is a typical karst topography. Due to the complex geological conditions and abundant rainfall, the area frequently experiences geological disasters. This paper selects Guilin as the study area, based on the geological and geomorphological, climatic and hydrological, and human activity factors of geohazards, and selects eight assessment indicators such as elevation, slope, faults, precipitation, water systems, normalized difference vegetation index (NDVI), roads, and land use types to establish an index system for the assessment of geohazards risk in Guilin City. Using the analytic hierarchy process (AHP) and the information model under the ArcGIS platform for data integration, the study area is assessed for geohazards risk. The results show that the proportions of extremely low, low, medium, high, and extremely high risk areas in Guilin City are 22.41%, 31.89%, 24.43%, 14.81%, and 6.45%, respectively. The extremely high-risk areas are mainly concentrated in the southwestern part of Guilin City, such as Yongfu County, Yangshuo County, and the urban area of Guilin; the extremely low-risk areas are mainly distributed in Longsheng County, Ziyuan County, Xing’an County, and other places; the rest of the areas have moderate risk levels, but exhibit a certain spatial differences. The model results indicate that precipitation may be the most important factor affecting the distribution of geohazards risk areas in Guilin City. The model assessment indicator (AUC) has a value of 0.796, which passes the accuracy test for the hazard assessment results. By integrating the natural geographical characteristics of the study area and the mechanisms of how each assessment indicator contributes to disasters, and comparing the hazard assessment results of typical regions, it is found that the high weight of assessment indicators such as precipitation and distance to faults, along with the medium and low weight distribution of other indicators, are quite reasonable. Furthermore, the weighted information quantity of each factor shows good correlation with its grading results, further validating the accuracy of this information quantity model. This study has assessed the impact of various disaster-causing factors on the level of geohazards risk in Guilin in detail and has delineated the geohazards risk areas in Guilin City, which has certain guiding significance for geohazards prevention and control, land use planning, and ecological and environmental protection in Guilin City and other related areas.

Key words: geohazards, risk assessment, AHP, information model, Guilin, China

CLC Number: P694

LÜ Shuang, LIU Qianyu, ZHANG Xiangru. Risk Assessment of Geohazards Using AHP-Information Model in Guilin, China[J].Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(3): 143-155.

References

[1] 刘传正,陈春利.中国地质灾害防治成效与问题对策[J].工程地质学报,2020,28(2):375-383. DOI: 10.13544/j.cnki.jeg.2019-232.
[2]刘传正,陈春利.中国地质灾害成因分析[J].地质论评,2020,66(5):1334-1348. DOI: 10.16509/j.georeview.2020.05.018.
[3]ZHENG Q, LYU H M, ZHOU A N, et al. Risk assessment of geohazards along Cheng-Kun railway using fuzzy AHP incorporated into GIS[J]. Geomatics Natural Hazards and Risk, 2021, 12(1): 1508-1531. DOI: 10.1080/19475705.2021.1933614.
[4]WARD P J, BLAUHUT V, BLOEMENDAAL N, et al. Review article: natural hazard risk assessments at the global scale[J]. Natural Hazards and Earth System Sciences, 2020, 20(4): 1069-1096. DOI: 10.5194/nhess-20-1069-2020.
[5]YILMAZ I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey)[J]. Computers & Geosciences, 2009, 35(6): 1125-1138. DOI: 10.1016/j.cageo.2008.08.007.
[6]AKGUN A. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey[J]. Landslides, 2012, 9(1): 93-106. DOI: 10.1007/s10346-011-0283-7.
[7]KAVZOGLU T, SAHIN E K, COLKESEN I. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression[J]. Landslides, 2014, 11(3): 425-439. DOI: 10.1007/s10346-013-0391-7.
[8]余淙蔚,柳侃,殷杰,等.一种适用于逻辑回归模型评价浅层滑坡易发性的网格尺度划分方法:以2019年福建省三明市群发浅层滑坡为例[J].山地学报,2022,40(1):106-119. DOI: 10.16089/j.cnki.1008-2786.000659.
[9]伍宇明,兰恒星,高星,等.一种基于贝叶斯理论的区域斜坡稳定性评价模型[J].工程地质学报,2014,22(6): 1227-1233. DOI: 10.13544/j.cnki.jeg.2014.06.029.
[10]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): 85-98. DOI: 10.1016/j.enggeo.2008.03.022.
[11]陈飞,郭顺,熊如宗,等.基于层次分析法的地质灾害危险性评价[J].有色金属科学与工程,2018,9(5):54-60. DOI: 10.13264/j.cnki.ysjskx.2018.05.010.
[12]张晓东,刘湘南,赵志鹏,等.基于层次分析法的盐池县地质灾害危险性评价[J].国土资源遥感,2019,31(3): 183-192. DOI: 10.6046/gtzyyg.2019.03.23.
[13]POURGHASEMI H R, PRADHAN B, GOKCEOGLU C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran[J]. Natural Hazards, 2012, 63(2): 965-996. DOI: 10.1007/s11069-012-0217-2.
[14]伍敏婷,孙岳,周子腾.基于层次分析-信息量法的滑坡易发性评价:以江西省浮梁县为例[J].东华理工大学学报(自然科学版),2023,46(2):157-166. DOI: 10.3969/j.issn.1674-3504.2023.02.007.
[15]郭佳,赵之星,刘志奇,等.结合信息量与AHP模型的阳泉市矿区地质灾害风险评价[J].测绘通报,2022(11):101-105. DOI: 10.13474/j.cnki.11-2246.2022.0333.
[16]ZHOU C H, CHEN M, CHEN J T, et al. A multi-hazard risk assessment model for a road network based on neural networks and fuzzy comprehensive evaluation[J]. Sustainability, 2024, 16(6): 2429. DOI: 10.3390/su16062429.
[17]蔡德所,马祖陆.漓江流域的主要生态环境问题研究[J].广西师范大学学报(自然科学版),2008,26(1):110-112. DOI: 10.3969/j.issn.1001-6600.2008.01.027.
[18]ZHAO H J, MA F S, GUO J. Regularity and formation mechanism of large-scale abrupt karst collapse in southern China in the first half of 2010[J]. Natural Hazards, 2012, 60(3): 1037-1054. DOI: 10.1007/s11069-011-9888-3.
[19]陈立华,李立丰,吴福,等.基于GIS与信息量法的北流市地质灾害易发性评价[J].地球与环境,2020,48(4):471-479. DOI: 10.14050/j.cnki.1672-9250.2020.48.060.
[20]张天祥,王艳霞,张雪珂,等.基于样本优化和机器学习的地质灾害气象风险预报模型研究:以云南省怒江州为例[J].广西师范大学学报(自然科学版):2025,43(1): 201-215. DOI: 10.16088/j.issn.1001-6600.2024022101.
[21]李蕃师.桂林市临桂区某石灰岩矿山地质环境恢复治理分区研究[D].桂林:桂林理工大学,2023. DOI: 10.27050/d.cnki.gglgc.2023.000215.
[22]覃小群,梁茂珍,陈阵.基于GIS的广西岩溶生态环境脆弱性评价[J].广西师范大学学报(自然科学版),2005,23(4):103-106. DOI: 10.3969/j.issn.1001-6600.2005.04.025.
[23]覃兰丽,曾宪斌,张云良.桂林市岩溶区典型地质灾害危险性评估[J].地质灾害与环境保护,2009,20(3):28-33. DOI: 10.3969/j.issn.1006-4362.2009.03.006.
[24]谢玲,陈展图,蒋瑜,等.基于InVEST&GEO-detectors模型的会仙湿地生境质量变化及影响因素研究[J].广西师范大学学报(自然科学版),2023,41(5):180-190. DOI: 10.16088/j.issn.1001-6600.2022071204.
[25]邓雪,李家铭,曾浩健,等.层次分析法权重计算方法分析及其应用研究[J].数学的实践与认识,2012,42(7): 93-100. DOI: 10.3969/j.issn.1000-0984.2012.07.012.
[26]CARRARA A. Multivariate models for landslide hazard evaluation[J]. Journal of the International Association for Mathematical Geology, 1983, 15(3): 403-426. DOI: 10.1007/BF01031290.
[27]寸得欣,令狐昌卫,马一奇,等.基于GIS和加权信息量模型的富源县地质灾害易发性评价[J].科学技术与工程,2024,24(18):7563-7573. DOI: 10.12404/j.issn.1671-1815.2304503.
[28]邓辉,何政伟,陈晔,等.信息量模型在山地环境地质灾害危险性评价中的应用:以四川泸定县为例[J].自然灾害学报,2014,23(2):67-76. DOI: 10.13577/j.jnd.2014.0210.
[29]刘晓.基于AHP的吉林省辽源市地质灾害危险性评价[J].首都师范大学学报(自然科学版),2022,43(5):35-43. DOI: 10.19789/j.1004-9398.2022.05.006.
[30]于开宁,吴涛,魏爱华,等.基于AHP-突变理论组合模型的地质灾害危险性评价:以河北平山县为例[J].中国地质灾害与防治学报,2023,34(2):146-155. DOI: 10.16031/j.cnki.issn.1003-8035.202201012.
[31]裴惠娟,陈晋,李雯,等.甘肃省暴雨洪水时空分布及风险评估[J].自然灾害学报,2017,26(3):167-175. DOI: 10.13577/j.jnd.2017.0320.
[32]廖春贵,陈月连,熊小菊,等.2007—2016年广西植被覆盖时空分布特征及其驱动因素[J].广西师范大学学报(自然科学版),2018,36(2):118-127. DOI: 10.16088/j.issn.1001-6600.2018.02.017.
[33]何东艳,华璀,卢远.漓江流域土地利用生态风险评价[J].广西师范大学学报(自然科学版),2012,30(4):172-178. DOI: 10.3969/j.issn.1001-6600.2012.04.027.
[34]肖雅丹,韦宝婧,黄泽斌,等.生态脆弱区公路选线优化及生态敏感性研究:以贵州省黔东南州玉屏至镇远公路为例[J].广西师范大学学报(自然科学版),2024,42(3):219-233. DOI: 10.16088/j.issn.1001-6600.2023070503.
[35]陈菊艳,朱斌,彭三曦,等.基于AHP和GIS的矿区岩溶塌陷易发性评估:以贵州林歹岩溶矿区为例[J].自然灾害学报,2021,30(5):226-236. DOI: 10.13577/j.jnd.2021.0522.
[36]邹凤钗,冷洋洋,陶小郎,等.基于斜坡单元的滑坡风险识别:以贵州万山浅层土质斜坡为例[J].中国地质灾害与防治学报,2022,33(3):114-122. DOI: 10.16031/j.cnki.issn.1003-8035.2022.03-13.
[37]卢志强,冷洋洋,赵罡.滑坡易发性评价的层次分析法研究:以贵州省普安县为例[J].贵州地质,2022,39(3):287-293. DOI: 10.3969/j.issn.1000-5943.2022.03.011.
[38]崔成敏.基于遥感的滑坡地质灾害研究:以云南鹤庆地区为例[D].北京:中国地质大学(北京),2020. DOI: 10.27493/d.cnki.gzdzy.2020.000311.
[39]赵正君.福建省龙岩市岩溶塌陷风险评估[D].北京:中国地质科学院,2010.
[40]莫运松,江思义,邹仁辉,等.基于专家-层次分析法的地质灾害风险性评价:以广西富川瑶族自治县为例[J].矿产勘查,2023,14(2):293-303. DOI: 10.20008/j.kckc.202302016.
[41]王运生,谢丙炎,万方浩,等.ROC曲线分析在评价入侵物种分布模型中的应用[J].生物多样性,2007,15(4): 365-372. DOI: 10.3321/j.issn:1005-0094.2007.04.005.
[42]胡祥祥,明璐璐,吴涛,等.融合InSAR与信息量-层次分析耦合模型的西宁市地质灾害易发性评价[J].测绘通报,2023(12):51-56,75. DOI: 10.13474/j.cnki.11-2246.2023.0358.
[43]LYU H M, SHEN J S, ARULRAJAH A. Assessment of geohazards and preventative countermeasures using AHP incorporated with GIS in Lanzhou, China[J]. Sustainability, 2018, 10(2): 304. DOI: 10.3390/su10020304.
[44]孟凡奇,高峰,林波,等.基于AHP和信息量模型的地质灾害易发性评价:以鲁东片区为例[J].灾害学,2023,38(3):111-117. DOI: 10.3969/J.ISSN.1000811X.2023.03.018.
[45]赵晓燕,谈树成,李永平.基于斜坡单元与组合赋权法的东川区地质灾害危险性评价[J].云南大学学报(自然科学版),2021,43(2):299-305. DOI: 10.7540/j.ynu.20200141.
[46]李福建,马安青,丁原东,等.基于RS与GIS技术的地质灾害危险性评价:以青岛市崂山区为例[J].中国海洋大学学报(自然科学版),2010,40(6):47-52, 152. DOI: 10.16441/j.cnki.hdxb.2010.06.007.
[47]欧阳成甫,张明华,梁锦叶.桂林市地热资源成热地质条件及其开发区预测分析[J].大地构造与成矿学,2002,26(1):97-100. DOI: 10.3969/j.issn.1001-1552.2002.01.017.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Risk Assessment of Geohazards Using AHP-Information Model in Guilin, China

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 8

Metrics

Comments

Recommended 0

[1]	XIAO Yadan, WEI Baojing, HUANG Zebin, YANG Fan, PENG Xing, WU Yi, XIONG Suwen. Optimization of Highway Route Selection and Ecological Sensitivity in Ecologically Fragile Areas: a Case Study of Yuping-Zhenyuan Highway, Guizhou, China [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(3): 219-233.
[2]	WANG Cui, SONG Yongchao, WU Linjian, ZHAO Xueting. Traffic Risk Assessment Method under Road Congestion Based on Improved DEMATEL-ISM Model [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(1): 28-38.
[3]	ZHOU Pengfei, LIU Lin, YANG Jinfan, LIU Tingting, WEI Guiyu, ZHOU Ruliang. Analysis on Spatial Distribution and Harm of Bats in Yunnan Province Based on Machine Learning [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(6): 139-149.
[4]	ZHONG Yanxia, CUI Wenbin, LUO Lingling, CHEN Feng, HE Jing. Spatial and Temporal Variation of Ecological Risk of Xinghai Lake in Ningxia Based on Landscape Pattern [J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(2): 154-161.
[5]	GUO Yongli, QUAN Xiqiang, WU Qing. Pollution Characteristics and Health Risk Assessment of Volatile Organic Compounds of Typical Karst Groundwater Source in North China [J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(6): 102-113.
[6]	PENG Limei, ZHAO Li, ZHOU Wu, HU Yueming. Risk Assessment of Heavy Metals in Cultivated Land in Conghua District of Guangzhou City, China [J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(5): 118-129.
[7]	LI Lujie,DONG Na,XIONG Feng,LUO Linglong,LI Hongyang. Research on Performance Evaluation of Construction Project Management Based on Cov-AHP and Connection Degree [J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(3): 111-119.
[8]	HE Dong-yan, HUA Cui, LU Yuan. Ecological Risk Assessment of Land Use in Lijiang River Basin [J]. Journal of Guangxi Normal University(Natural Science Edition), 2012, 30(4): 172-178.