Landslide susceptibility zonation using the analytical hierarchy process. A case study of Guantanamo Province

The creation of predictive models to determine areas susceptible to landslides is important for timely management towards disaster prevention of these phenomena. The present study in the province of Guantanamo in Cuba had as main objective the landslide susceptibility zonation (LSZ). The multicriteria decision method used by Saaty (1980) was adopted, considering the factors; slope angle, elevation, distance to rivers, distance to fault, distance to roads, average annual rainfall, lithology, soil depth, soil type, texture and vegetation. Factor weights were determined through the Analytical Hierarchy Process (AHP) and factor class ratings were assigned through logical judgment. Landslide susceptibility indices were determined based on a continuous numerical scale developed for this purpose. It was found that the zones of high and moderate susceptibility corresponded to the northeast of the Guantánamo province, which are characterized by a high density of faults and the hydrological network, shallow soils with clayey composition. This zone is also constituted fundamentally by rocks of the metamorphic and ophiolitic complexes, in general, very structurally affected. The receiver operating characteristic (ROC) curve showed acceptable results. In addition, the risk assessment indicated that populations at high to very high risk.

Keywords: Analytical Hierarchy Process (AHP), Guantanamo, landslide susceptibility zonation, Cuba, landslides, gravitational processes, landslide hazard, nine-point importance scale, according to Saaty.
For citation:

Pospehov G. B., Savón Yu., Moseykin V. V. Landslide susceptibility zonation using the analytical hierarchy process. A case study of Guantanamo Province. MIAB. Mining Inf. Anal. Bull. 2024;(1):125-145. DOI: 10.25018/0236_1493_2024_1_0_125.

Acknowledgements:
Issue number: 1
Year: 2024
Page number: 125-145
ISBN: 0236-1493
UDK: 624.131.439.3
DOI: 10.25018/0236_1493_2024_1_0_125
Article receipt date: 02.10.2023
Date of review receipt: 07.11.2023
Date of the editorial board′s decision on the article′s publishing: 10.12.2023
About authors:

G.B. Pospehov1, Cand. Sci. (Geol. Mineral.), Assistant Professor, e-mail: pospehov@spmi.ru, ORCID ID: 0000-0001-9090-5150, 
Yu. Savón1, Graduate Student, e-mail: s215003@stud.spmi.ru, ORCID ID: 0000-0002-9640-8478,
V.V. Moseykin, Dr. Sci. (Eng.), Professor, e-mail: moseykin@inbox.ru, ORCID ID: 0000-0002-2286-1480, National University of Science and Technology «MISiS», 119049, Moscow, Russia,
1 Empress Catherine II Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia.

 

For contacts:

G.B. Pospehov, e-mail: pospehov@spmi.ru.

Bibliography:

1. de Assis Dias M. C., Saito S. M., dos Santos Alvalá R. C., Seluchi M. E., Bernardes T., Camarinha P. I. M., Stenner C., Nobre C. A. Vulnerability index related to populations at-risk for landslides in the Brazilian Early Warning System (BEWS). International Journal of Disaster Risk Reduction. 2020, vol. 49, article 101742. DOI: 10.1016/j.ijdrr.2020.101742.

2. Du J., Glade T., Woldai T., Chai B., Zeng B. Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Engineering Geology. 2020, vol. 270, article 105572. DOI: 10.1016/j.enggeo.2020.105572.

3. Antronico L., De Pascale F., Coscarelli R., Gullà G. Landslide risk perception, social vulnerability and community resilience: The case study of Maierato (Calabria, Southern Italy). International Journal of Disaster Risk Reduction. 2020, vol. 46, article 101529. DOI: 10.1016/j.ijdrr.2020.101529.

4. Hernandez Aguilar B., Ruiz Rivera N. The production of vulnerability to landslides: The risk habitus in two landslide-prone neighborhoods in Teziutlán, Mexico. Investigaciones Geográficas, Boletín del Instituto de Geografía. 2016, no. 90, pp. 7—27. DOI: 10.14350/rig.50663.

5. Mirdda H. A., Bera S., Chatterjee R. Vulnerability assessment of mountainous households to landslides. A multidimensional study in the rural Himalayas. International Journal of Disaster Risk Reduction. 2022, vol. 71, article 102809. DOI: 10.1016/j.ijdrr.2022.102809.

6. Paul A., Deka J., Gujre N., Rangan L., Mitra S. Does nature of livelihood regulate the urban community’s vulnerability to climate change? Guwahati city, a case study from North East India. Journal of Environmental Management. 2019, vol. 251, article 109591. DOI: 10.1016/j. jenvman.2019.109591.

7. Papathoma-Köhle M., Zischg A., Fuchs S., Glade T., Keiler M. Loss estimation for landslides in mountain areas — An integrated toolbox for vulnerability assessment and damage documentation. Environmental Modelling & Software. 2015, vol. 63, pp. 156—169. DOI: 10.1016/j. envsoft.2014.10.003.

8. Xiao Y., Tang X., Li Y., Huang H., An B.-W. Social vulnerability assessment of landslide disaster based on improved TOPSIS method: Case study of eleven small towns in China. Ecological Indicators. 2022, vol. 143, article 109316. DOI: 10.1016/j.ecolind.2022.109316.

9. Gupta A. K., Negi M., Nandy S., Alatalo J. M., Singh V., Pandey R. Assessing the vulnerability of socio-environmental systems to climate change along an altitude gradient in the Indian Himalayas. Ecological Indicators. 2019, vol. 106, article 105512. DOI: 10.1016/j.ecolind.2019.105512.

10. Aghdam I. N., Varzandeh M. H. M., Pradhan B. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environmental Earth Science. 2016, vol. 75, article 553. DOI: 10.1007/ s12665-015-5233-6.

11. Chen W., Panahi M., Tsangaratos P., Shahabi H., Ilia I., Panahi S., Li S., Jaafari A., Ahmad B. B. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena. 2019, vol. 172, pp. 212—231. DOI: 10.1016/j.catena.2018.08.025.

12. Glazunov V. V., Burlutsky S. B., Shuvalova R. A., Zhdanov S. V. Improving the reliability of 3D modelling of a landslide slope based on engineering geophysics data. Journal of Mining Institute. 2022, vol. 257, pp. 771—782. [In Russ]. DOI: 10.31897/PMI.2022.86.

13. Chang Z., Huang F., Huang J., Jiang S.-H., Liu Y., Meena S. R., Catani F. An updating of landslide susceptibility prediction from the perspective of space and time. Geoscience Frontiers. 2023, vol. 14, no. 5, article 101619. DOI: 10.1016/j.gsf.2023.101619.

14. Das J., Saha P., Mitra R., Alam A., Kamruzzaman M. GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India. Heliyon. 2023, vol. 9, no. 5, article e16186. DOI: 10.1016/j.heliyon.2023.e16186.

15. Goetz J. N., Brenning A., Petschko H., Leopold P. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences. 2015, vol. 81, pp. 1—11. DOI: 10.1016/j.cageo.2015.04.007.

16. Kutepova N. A., Moseykin V. V., Kondakova V. N., Pospehov G. B., Straupnik I. A. Specificity of properties of coal processing waste regarding their storage. MIAB. Mining Inf. Anal. Bull. 2022, no. 12, pp. 77—93. [In Russ]. DOI: 10.25018/0236_1493_2022_12_0_77.

17. Poiraud A. Landslide susceptibility-certainty mapping by a multi-method approach. A case study in the Tertiary basin of Puy-en-Velay (Massif central, France). Geomorphology. 2014, vol. 216, pp. 208—224. DOI: 10.1016/j.geomorph.2014.04.001.

18. Guimpier A., Conway S. J., Pajola M., Luchetti A., Simioni E., Re C., Noblet A., Mangold N., Thomas N., Cremonese G. CaSSIS Team. Pre-landslide topographic reconstruction in Baetis Chaos, mars using a CaSSIS Digital Elevation Model. Planetary and Space Science. 2022, vol. 218, article 105505. DOI: 10.1016/j.pss.2022.105505

19. Kazantsev A., Boikov V., Valkov V. Monitoring the deformation of the earth’s surface in the zone of influence construction. E3S Web of Conferences. 2020, vol. 157, article 02013. DOI: 10.1051/e3sconf/202015702013.

20. Mazurov B. T., Mustafin M. G., Panzhin A. A. Estimation method for vector field divergence of earth crust deformations in the process of mineral deposits development. Journal of Mining Institute. 2019, vol. 238, pp. 376—382. [In Russ]. DOI: 10.31897/PMI.2019.4.376.

21. Pavlovich A. A., Korshunov V. A., Bazhukov A. A., Melnikov N. Y. Estimation of rock mass strength in open-pit mining. journal of mining institute. Journal of Mining Institute. 2019, vol. 239, pp. 502—509. [In Russ]. DOI: 10.31897/PMI.2019.5.502.

22. Protosenya A. G., Lebedev M. O., Karasev M. A., Belyakov N. A. Geomechanics of low-subsidence construction during the development of underground space in large cities and megalopolises. Journal of Mechanical and Production Engineering Research and Development. 2019, vol. 9, no. 5, pp. 1005—1019.

23. Asmare D. Landslide hazard zonation and evaluation around Debre Markos town, NW Ethiopia—A GIS-based bivariate statistical approach. Scientific African. 2022, vol. 15, article e01129. DOI: 10.1016/j.sciaf.2022.e01129.

24. Ayalew L., Yamagishi H. The application of GISbased logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 2005, vol. 65, pp. 15—31. DOI: 10.1016/j.geomorph.2004.06.010.

25. Castellanos E., Van Westen C. J. Qualitative landslide susceptibility assessment by multicriteria analysis. A case study from San Antonio del Sur, Guantánamo, Cuba. Geomorphology. 2008, vol. 94, no. 3-4, pp. 453—466. DOI: 10.1016/j.geomorph.2006.10.038.

26. Aleotti P. A warning system for rainfall-induced shallow failures. Engineering Geology. 2004, vol. 73, pp. 247—265. DOI: 10.1016/j.enggeo.2004.01.007.

27. Huang F., Chen J., Liu W., Huang J., Hong H., Chen W. Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold. Geomorphology. 2022, vol. 408, article 108236. DOI: 10.1016/j.geomorph.2022.108236.

28. Larsen M. C., Simon A. A Rainfall intensity-duration threshold for landslides in a humidtropical environment, Puerto Rico. Geografiska Annaler. 1993, vol. 75A, no. 1-2, pp. 13—23.

29. Liao W., Ji L. Time-dependent reliability analysis of rainfall-induced shallow landslides considering spatial variability of soil permeability. Computers and Geotechnics. 2021, vol. 129, article 103903. DOI: 10.1016/j.compgeo.2020.103903.

30. Zhao B., Dai Q., Zhuo L., Mao J., Zhu S., Han D. Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting. Geomorphology. 2022, vol. 398, article 108051. DOI: 10.1016/j.geomorph.2021.108051.

31. Huang F., Xiong H., Yao C., Catani F., Zhou C., Huang J. Uncertainties of landslide susceptibility prediction considering different landslide types. Journal of Rock Mechanics and Geotechnical Engineering. 2023. DOI: 10.1016/j.jrmge.2023.03.001.

32. Sun H., Li W., Scaioni M., Fu J., Guo X., Gao J. Influence of spatial heterogeneity on landslide susceptibility in the transboundary area of the Himalayas. Geomorphology. 2023, vol. 433, article 108723. DOI: 10.1016/j.geomorph.2023.108723.

33. Flavie L. Z., Moussa N. N., Moïse C. B. A., Germain Marie Monespérance M., Pascal Landry W. D., Rodrigue T. K., Armand K. D., Sébastien O. Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Bafoussam-Dschang region (West Cameroon). Advances in Space Research. 2023, vol. 71, pp. 5282—5301. DOI: 10.1016/j.asr.2023.02.014.

34. Ahmad M. S., MonaLisa, Khan S. (2023) Comparative analysis of analytical hierarchy process (AHP) and frequency ratio (FR) models for landslide susceptibility mapping in Reshun, NW Pakistan. Kuwait Journal of Science. 2023, vol. 50, no. 3, pp. 387—398. DOI: 10.1016/j. kjs.2023.01.004.

35. De Feo G., De Gisi S. Using an innovative criteria weighting tool for stakeholders involvement to rank MSW facility sites with the AHP. Special Thematic Section: Sanitary Landfilling. 2010, vol. 30, no. 11, pp. 2370—2382. DOI: 10.1016/j.wasman.2010.04.010.

36. Saaty T. L. The analytical hierarchy process, planning, priority. London, 1980. 287 p.

37. Arabameri A., Pradhan B., Rezaei K., Conoscenti C. Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. Catena. 2019, vol. 180, pp. 282—297. DOI: 10.1016/j.catena.2019.04.032.

38. Dom N. C., Ahmad A. H., Latif Z. A., Ismail R. Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pacific Journal of Tropical Disease. 2016, vol. 6, no. 12, pp. 928—935. DOI: 10.1016/S2222-1808(16)61158-1.

39. Mandal B., Mandal S. Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Advances in Space Research. 2018, vol. 62, no. 11, pp. 3114—3132. DOI: 10.1016/j.asr.2018.08.008.

40. Fanos A. M., Pradhan B. A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS. Catena, vol. 172, pp. 435—450. DOI: 10.1016/j.catena.2018.09.012.

41. Taheri K., Gutiérrez F., Mohseni H., Raeisi E., Taheri M. Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude-frequency relationships. A case study in Hamadan province, Iran. Geomorphology. 2015, vol. 234, pp. 64—79. DOI: 10.1016/j. geomorph.2015.01.005.

42. Pospehov G. B., Savón Y., Delgado R., Castellanos E., Peña A. Inventory of landslides triggered by hurricane matthews in Guantánamo, Cuba. Geography, Environment, Sustainability. 2023, vol. 16, no. 1, pp. 55—63. DOI: 10.24057/2071-9388-2022-133.

43. Malczewski J. GIS and multicriteria decision analysis, New York, Wiley, 1999. 411 p.

44. Esmaeilpour-Poodeh S., Ghorbani R., Hosseini S. A., Salmanmahiny A., Rezaei H., Kamyab H. Amulti-criteria evaluation method for sturgeon farming site selection in the southern coasts of the Caspian Sea. Aquaculture. 2019, vol. 513, article 734416. DOI: 10.1016/j.aquaculture.2019.734416.

45. Jazouli A. E., Barakat A., Khellouk R., Rais J., Baghdadi M. E. Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sensing Applications: Society and Environment. 2019, vol. 13, pp. 361—374. DOI: 10.1016/j.rsase.2018.12.004.

46. Panchal S., Shrivastava A. Kr. Landslide hazard assessment using analytic hierarchy process (AHP). A case study of National Highway 5 in India. Ain Shams Engineering Journal. 2022, vol. 13, no. 3, article 101626. DOI: 10.1016/j.asej.2021.10.021.

47. Saaty T. L. Theory and applications of the analytic network process: Decision making with benefits, opportunities, costs, and risks. Pittsburgh: RWS Publications. 2005, 352 p.

48. Saaty T. L. Decision Making with the Analytic Hierarchy Process. International Journal of Services Sciences. 2008, vol. 1, 83 p. DOI: 10.1504/IJSSCI.2008.017590.

49. Saaty T. L. Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsburgh, 1994. 527 p.

50. Volohov E. M., Kozhukharova V. K., Britvin I. A., Savkov B. M., Zherlygina E. S. Assessment of impact of mining operations on surface infrastructure. MIAB. Mining Inf. Anal. Bull. 2023, no. 8, pp. 72—93. [In Russ]. DOI: 10.25018/0236_1493_2023_8_0_72.

51. GolikV. I., Marinin M.A. Practice of underground leaching of uranium in blocks. MIAB. Mining Inf. Anal. Bull. 2022, no. 6-1, pp. 5—20. [In Russ]. DOI: 10.25018/0236_1493_2022_61_0_5.

52. Fomin S. I., Ivanov V. V., Semenov A. S., Ovsyannikov M. P. Incremental open-pit mining of steeply dipping ore deposits. ARPN Journal of Engineering and Applied Sciences. 2020, vol. 15, no. 11, pp. 1306—1311.

53. Kovalski E. R., Kongar-Syuryun Ch. B., Petrov D. N. Challenges and prospects for several-stage stoping in potash minining. Sustainable Development of Mountain Territories. 2023, vol. 15, no. 2, pp. 349—364. [In Russ]. DOI: 10.21177/1998-4502-2023-15-2-349-364.

54. Smirnov Y. D., Suchkov D. V., Danilov A. S., Goryunova T. V. Artificial soils for restoration of disturbed land productivity. Eurasian Mining. 2021, no. 2, pp. 92—96. DOI: 10.17580/ em.2021.02.19.

55. Petrova T. A., Rudzisha E., Alekseenko A. V., Bech J., Pashkevich M. A. Rehabilitation of disturbed lands with industrial wastewater sludge. Minerals. 2022. No. 12, pp. 376—376.

56. Alemayo G. G., Eritro T. H. Landslide vulnerability of the Debre Sina-Armania road section, Central Ethiopia: Insights from geophysical investigations. Journal of African Earth Sciences. 2021, vol. 184, article 104383. DOI: 10.1016/j.jafrearsci.2021.104383.

57. Jiang X., Xu D., Rong J., Ai X., Ai S., Su X., Sheng M., Yang S., Zhang J., Ai Y. Landslide and aspect effects on artificial soil organic carbon fractions and the carbon pool management index on road-cut slopes in an alpine region. Catena. 2021, vol. 199, article 105094. DOI: 10.1016/j.catena.2020.105094.

58. Liu Q., Tang A., Huang D., Huang Z., Zhang B., Xu X. Total probabilistic measure for the potential risk of regional roads exposed to landslides. Reliability Engineering & System Safety. 2022, vol. 228, article 108822. DOI: 10.1016/j.ress.2022.108822.

59. Mauri L., Straffelini E., Tarolli P. Multi-temporal modeling of road-induced overland flow alterations in a terraced landscape characterized by shallow landslides. International Soil and Water Conservation Research. 2022, vol. 10, no. 2, pp. 240—253. DOI: 10.1016/j.iswcr.2021.07.004.

60. Zhao B., Dai Q., Han D., Dai H., Mao J., Zhuo, L. Probabilistic thresholds for landslides warning by integrating soil moisture conditions with rainfall thresholds. Journal of Hydrology. 2019, vol. 574, pp. 276—287. DOI: 10.1016/j.jhydrol.2019.04.062.

61. Huang J., Ju N. P., Liao Y. J., Liu D. D. Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method. Natural Hazard and Earth System Sciences. 2015, vol. 15, pp. 2715—2723. https://nhess.copernicus.org/articles/15/2715/2015.

62. Crosta G. B. Regionalization of rainfall thresholds: An aid to landslide hazard evaluation. Environmental Geology. 1998, vol. 35, pp. 131—145. DOI: 10.1007/s002540050300.

63. Finlay P. J., Fell R., Maguire P. K. The relationship between the probability of landslide occurrence and rainfall. Canadian Geotechnical Journal. 1997, vol. 36, pp. 811—824. DOI: 10.1139/t97-047.

64. Garland G. G., Oliver M. J. Predicting landslides from rainfall in a humid, sub-tropical region. Geomorphology, 1993, vol. 8, pp. 165—173. DOI: 10.1016/0169-555X(93)90035-Z.

65. Kay J. N. Rainfall-landslide relationship for Hong Kong. Proceeding ICE. Geotechnical Engineering. 1995, vol. 113, pp. 117—118. DOI: 10.1680/igeng.1995.27592.

66. Vennari C., Gariano S. L., Antronico L., Brunetti M. T., Iovine G., Peruccacci S., Terranova O., Guzzetti F. Rainfall thresholds for shallow landslide occurrence in Calabria, Southern Italy. Natural Hazard and Earth System Sciences. 2014, vol. 14, article 14.

67. Raghuvanshi T. K., Ibrahim J., Ayalew D. Slope stability susceptibility evaluation parameter (SSEP) rating scheme — An approach for landslide hazard zonation. Special Volume of the 24th Colloquium of African Geology. 2014, vol. 99, pp. 595—612. DOI: 10.1016/j.jafrearsci.2014.05.004.

68. Mekonnen A. A., Raghuvanshi T. K., Suryabhagavan K. V., Kassawmar T. GIS-based landslide susceptibility zonation and risk assessment in complex landscape. A case of Beshilo watershed, Northern Ethiopia. Environmental Challenges. 2022, vol. 8, article 100586. DOI: 10.1016/ j.envc.2022.100586.

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