Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China

  • Li-ping Liao
  • Ying-yan Zhu
  • Yan-lin Zhao
  • Hai-Tao WenEmail author
  • Yun-chuan Yang
  • Li-hua Chen
  • Shao-kun Ma
  • Ying-zi Xu


Landslides distribute extensively in Rongxian county, the southeast of Guangxi province, China and pose great threats to this county. At present, hazard management strategy is facing an unprecedented challenge due to lack of a landslide susceptibility map. Therefore, the purpose of this paper was to construct a landslide susceptibility map by adopting three widely used models based on an integrated understanding of landslide’s characteristics. These models include a semi-quantitative method (SQM), information value model (IVM) and logistical regression model (LRM).The primary results show that (1) the county is classified into four susceptive regions, named as very low, low, moderate and high, which covered an area of 13.43%, 32.40%, 31.19% and 22.99% in SQM, 0.86%, 26.82%, 44.11%, and 28.21% in IVM, 9.88%, 17.73%, 46.36% and 26.03% in LRM; (2) landslides are likely to occur within the areas characterized by following obvious aspects: high intensity of human activities, slope angles of 25°~35°, the thickness of weathered soil is larger than 15 m; the lithology is granite, shale and mud rock; (3) the area under the curve of SQM, IVM and LRM is 0.7151, 0.7688 and 0.7362 respectively, and the corresponding success rate is 71.51%, 76.88% and 73.62%. It is concluded that these three models are acceptable because they have an effective capability of susceptibility assessment and can achieve an expected accuracy. In addition, the susceptibility outcome obtained from IVM provides a slightly higher quality than that from SQM, LRM.


Landslide characteristic Susceptibility zonation Prevention regionalization Rongxian county 


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This research was funded by the National Natural Science Foundation of China (No. 51609041), the Natural Scientific Project of Guangxi Zhuang Autonomous Region (No. 2018GXNSFAA138187), the Project of the Education Department of Guangxi Zhuang Autonomous Region (No. 2018KY0027), and the Project of Department of Land and Resources of Guangxi Zhuang Autonomous Region (GXZC2018-G3-19302- JGYZ).


  1. Abdallah C, Faour G (2016) Landslide hazard mapping of Ibrahim River Basin, Lebanon. Natural Hazards 85(1): 237–266. Google Scholar
  2. Achour Y, Boumezbeur A, Hadji R, et al. (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian Journal of Geosciences 10(8): 194–209. Google Scholar
  3. Ada M, San BT (2018) Comparison of machine-learning techniques for landslide susceptibility mapping using twolevel random sampling (2LRS) in Alakir catchment area, Antalya, Turkey. Natural Hazards 90(1): 237–263. Google Scholar
  4. Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology 54(6): 1127–1143. Google Scholar
  5. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2): 15–31. Google Scholar
  6. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1): 73–81. Google Scholar
  7. Bourenane H, Guettouche MS, Bouhadad Y, et al. (2016) Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods. Arabian Journal of Geosciences 9(2): 1–24. Google Scholar
  8. Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12(3): 419–436. Google Scholar
  9. Che VB et al. (2012) Landslide susceptibility assessment in Limbe (SW Cameroon): A field calibrated seed cell and information value method. Catena 92: 83–98. Google Scholar
  10. Chen LX, Westen CJv, Hussin H, et al. (2016a) Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps. Geomorphology 273: 150–167. Google Scholar
  11. Chen T, Niu RQ, Du B, et al. (2015a) Landslide Spatial Susceptibility Mapping by Using GIS and Remote Sensing Techniques. Environmental Earth Sciences 73(9): 5571–5583. Google Scholar
  12. Chen T, Niu RQ, Jia XP (2016b) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environmental Earth Sciences 75(10): 866–891. Google Scholar
  13. Chen W, Li WP, Chai HC, et al. (2015b) GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environmental Earth Sciences 75(1): 63–76. Google Scholar
  14. Chen W, Li WP, Hou EK, et al. (2014) Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China. Arabian Journal of Geosciences 7(11): 4499–4511. Google Scholar
  15. China geological survey (2008) The investigation standard of landslide and debris flow (1:50000). Technical standard for geological survey (NoDD2008-02).Google Scholar
  16. Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 30(3):451–472. Google Scholar
  17. Dai FC, Lee CF, Li J, et al. (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology 40(3): 381–391Google Scholar
  18. Deng YC, Tsai F, Hwang JH (2016) Landslide characteristics in the area of Xiaolin Village during Morakot typhoon. Arabian Journal of Geosciences 9(5): 332–347. Google Scholar
  19. Devkota KC et al. (2012) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards 65(1): 135–165. Google Scholar
  20. Du GL, Zhang YS, Iqbal J, et al. (2017) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science 14(2): 249–268. Google Scholar
  21. Fall M, Azzam R, Noubactep C (2006) A multi-method approach to study the stability of natural slopes and landslide susceptibility mapping. Engineering Geology 82(4): 241–263. Google Scholar
  22. Ghobadi MH, Nouri M, Saedi B, et al. (2017) The performance evaluation of information value, density area, LNRF, and frequency ratio methods for landslide zonation at Miandarband area, Kermanshah Province, Iran. Arabian Journal of Geosciences 10(19): 430–444. Google Scholar
  23. Guns M, Vanacker V (2012) Logistic regression applied to natural hazards: rare event logistic regression with replications. Natural Hazards and Earth System Science 12(6): 1937–1947. Google Scholar
  24. Hadmoko DS, Lavigne F, Sartohadi J, et al. (2010) Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia. Natural Hazards 54(3): 623–642. Google Scholar
  25. Hong HY, Chen W, Xu C, et al. (2016a) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto International 32(2): 139–154. Google Scholar
  26. Hong HY et al. (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena 163: 399–413. Google Scholar
  27. Hong HY et al. (2017a) A novel hybrid integration model using support vector machines and random subspace for weathertriggered landslide susceptibility assessment in the Wuning area (China). Environmental Earth Sciences 76(19): 652–670. Google Scholar
  28. Hong HY, Llia I, Tsangaratos P, et al. (2017b) A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology 290: 1–16. Google Scholar
  29. Hong HY, Pourghasemi HR, Pourtaghi ZS (2016b) Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259: 105–118. Google Scholar
  30. Hong HY, Pradhan B, Xu C, et al. (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133: 266–281. Google Scholar
  31. Johnson K, Depietri Y, Breil M (2016) Multi-hazard risk assessment of two Hong Kong districts. International Journal of Disaster Risk Reduction 19: 311–323. Google Scholar
  32. Kayastha P (2015) Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from Garuwa sub-basin, East Nepal. Arabian Journal of Geosciences 8(10): 8601–8613. Google Scholar
  33. Kouli M, Loupasakis C, Soupios P, et al. (2010) Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece. Natural Hazards 52(3): 599–621. Google Scholar
  34. Lara M, Sepúlveda SA (2009) Landslide susceptibility and hazard assessment in San Ramón Ravine, Santiago de Chile, from an engineering geological approach. Environmental Earth Sciences 60(6): 1227–1243. Google Scholar
  35. Le QH, Nguyen THV, Do MD, et al. (2016) Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam). Landslides 13(5): 1285–1301. Google Scholar
  36. Lee J-H, Sameen MI, Pradhan B, et al. (2018) Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology 303: 284–298. Google Scholar
  37. Lee S, Hong SM, Jung HS (2017) A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea. Sustainability 9(1): 1–15. Google Scholar
  38. Lee S, Pradhan B (2006) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1): 33–41. Google Scholar
  39. Leventhal AR, Kotze GP (2008) Landslide susceptibility and hazard mapping in Australia for land-use planning — with reference to challenges in metropolitan suburbia. Engineering Geology 102(3–4): 238–250. Google Scholar
  40. Li YY (2007) Causes analysis and preventive countermeasures of geological disasters in Guangxi Province. Journal of Anhui agricultural science 35(36):11941–11943 (in Chinese)Google Scholar
  41. Lin L, Lin QG, Wang Y (2017) Landslide susceptibility mapping on a global scale using the method of logistic regression. Natural Hazards and Earth System Sciences 17(8): 1411–1424. Google Scholar
  42. Liu CZ (2014) Genetic types of landslide and debris flow disasters in China. Geological Review 60(4): 858–868. (in Chinese)Google Scholar
  43. Liu YH, Wen MS, Su YC, et al. (2016) Characteristics of geohazards induced by typhoon rainstorm and evaluation of geohazards early warning. Hydrology and Engineering Geology 43(5): 119–126. (in Chinese)Google Scholar
  44. Lovine GGR, Greco R, Gariano SL, et al. (2014) Shallowlandslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Natural Hazards 73(1): 111–136. Google Scholar
  45. Meng QK, Miao F, Zhen J, et al. (2016) GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bulletin of Engineering Geology and the Environment 75(3): 923–944. Google Scholar
  46. Myronidis D, Papageorgiou C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Natural Hazards 81(1): 245–263. Google Scholar
  47. Park S, Choi C, Kim B, et al. (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental Earth Sciences 68(5): 1443–1464. Google Scholar
  48. Pourghasemi HR, Pradhan B, Gokceoglu C, et al. (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences 6(7): 2351–2365. Google Scholar
  49. Quan HC, Lee BG (2012) GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea). Ksce Journal of Civil Engineering 16(7): 1258–1266. Google Scholar
  50. Raghuvanshi TK, Ibrahim J, Ayalew D (2014) Slope stability susceptibility evaluation parameter (SSEP) rating scheme–An approach for landslide hazard zonation. Journal of African Earth Sciences 99: 595–612. Google Scholar
  51. Sakkas G, Misailidis I, Sakellariou N, et al. (2016) Modeling landslide susceptibility in Greece: a weighted linear combination approach using analytic hierarchical process, validated with spatial and statistical analysis. Natural Hazards 84(3): 1873–1904. Google Scholar
  52. Sarkar S, Roy AK, Martha TR (2013) Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas. Journal of the Geological Society of India 82(4): 351–362. Google Scholar
  53. Singh K, Kumar V (2017) Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio. Arabian Journal of Geosciences 10(24): 539–556. Google Scholar
  54. Sujatha ER, Rajamanickam GV (2015) Landslide Hazard and Risk Mapping Using the Weighted Linear Combination Model Applied to the Tevankarai Stream Watershed, Kodaikkanal, India. Human and Ecological Risk Assessment 21(6): 1445–1461. Google Scholar
  55. Sujatha ER, Rajamanickam GV, Kumarave P (2012) Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: A case study on Tevankarai stream watershed, India. Journal of Earth System Science 121(5): 1337–1350Google Scholar
  56. Tsangaratos P, Llia I, Hong HY, et al. (2016) Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14(3): 1091–1111. Google Scholar
  57. Umar Z, Pradhan B, Ahmad A, et al. (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118: 124–135. Google Scholar
  58. Vahidnia MH, Alesheikh AA, Alimohammadi A, et al. (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Computers & Geosciences 36(9): 1101–1114. Google Scholar
  59. Wang DJ, He QS (2009) Characteristics analysis of geologcial hazards caused by heavy rainfall in recent year in Guangxi province. Journal of Anhui agricultural science 37(18): 8595–8596 (In Chinese)Google Scholar
  60. Wang GQ, Xu W, Wu DX, et al. (2004) Characteristic of environmental geology and geological disasters of Anhui province. Chinese Journal of Rock Mechanics and Engineering 23(1): 164–169 (in Chinese)Google Scholar
  61. Wang M, Liu M, Yang S, et al. (2014) Incorporating Triggering and Environmental Factors in the Analysis of Earthquake-Induced Landslide Hazards. International Journal of Disaster Risk Science 5(2): 125–135. Google Scholar
  62. Wang QQ et al. (2015) Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment. Sustainability 7(12): 16653–16669. Google Scholar
  63. Wang XL, Zhang LQ, Wang SJ, et al. (2013) Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides 11(3): 399–409. Google Scholar
  64. Wei CH, Wen HT, Liao LP, et al. (2017) Failure Characteristics and Prevention Measures of Granite Residual Soil Slope in the Southeast of Guangxi Province, China. Earth and Environment 45(5): 576–585. (In Chinese)Google Scholar
  65. Wen HT (2015) A detailed survey report of geological disasters in Rongxian County, Guangxi. Guangxi Zhuang Autonomous Region Geological Environmental Monitoring Station, Guilin, China. (In Chinese)Google Scholar
  66. Wen HT, Wei CH, Liao LP, et al. (2017) Occurrence and Temporal-spatial Distribution of Geological Hazards in Rongxian county of Southeast Guangxi Bulletin of Soil and Water Conservation 37(5): 182–188/197. (in Chinese)Google Scholar
  67. Wu YL, Li WP, Liu P, et al. (2016a) Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environmental Earth Sciences 75(5): 422–432. Google Scholar
  68. Wu YL, Li WP, Wang QQ, et al. (2016b) Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arabian Journal of Geosciences 9(2): 84–99. Google Scholar
  69. Xu C, Xu XW, Dai FC, et al. (2012) Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region. Journal of Earth Science 23(1): 97–120. Google Scholar
  70. Xu Y, Xu XR, Tang Q (2016) Human activity intensity of land surface: Concept, methods and application in China. Journal of Geographical Sciences 26(9): 1349–1361. Google Scholar
  71. Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena 72(1): 1–12. Google Scholar
  72. Yamao M, Sidle RC, Gomi T, et al. (2016) Characteristics of landslides in unwelded pyroclastic flow deposits, southern Kyushu, Japan. Natural Hazards and Earth System Sciences 16(2): 617–627. Google Scholar
  73. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Computers & Geosciences 35(6): 1125–1138. Google Scholar
  74. Youssef AM, Pourghasemi HR, Pourtaghi ZS, et al. (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5): 839–856. Google Scholar
  75. Yu B, Wang T, Zhu Y, et al. (2016) Topographical and rainfall factors determining the formation of gully-type debris flows caused by shallow landslides in the Dayi area, Guizhou Province, China. Environmental Earth Sciences 75(7): 551–568. Google Scholar
  76. Yusof NM, Pradhan B, Shafri HZM, et al. (2015) Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data. Arabian Journal of Geosciences 8(11): 9789–9800. Google Scholar
  77. Zhang GF, Cai YX, Zheng Z, et al. (2016) Integration of the Statistical Index Method and the Analytic Hierarchy Process technique for the assessment of landslide susceptibility in Huizhou, China. Catena 142: 233–244. Google Scholar
  78. Zhao CX, Chen W, Wang QQ, et al. (2015) A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China. Arabian Journal of Geosciences 8(11): 9079–9088. Google Scholar
  79. Zhou C, Yin KL, Cao Y, et al. (2018) Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers & Geosciences 112: 23–37. Google Scholar
  80. Zhou CH, Cheng WM, Qian JK, et al. (2009) Research on the Classification System of Digital Land Geomorphology of 1:1000000 in China. Journal of Geo-Information Science 11(6): 707–724 (In Chinese)Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Civil Engineering and ArchitectureGuangxi UniversityNanningChina
  2. 2.Key Laboratory of Disaster Prevention and Structural Safety of Ministry of EducationGuangxi UniversityNanningChina
  3. 3.Guangxi Key Laboratory of Disaster Prevention and Engineering SafetyGuangxi UniversityNanningChina
  4. 4.Key Laboratory of Mountain Hazards and Surface Processes, Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina
  5. 5.Guangxi Zhuang Autonomous Region Geological Environmental Monitoring StationGuilinChina

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