Advertisement

GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria

  • O. H. OziokoEmail author
  • O. Igwe
Article

Abstract

Udi-Iva Valley region of Enugu state has the most concentration of landslide in Southeastern Nigeria. Detailed field investigations alongside satellite image studies were employed to delineate nine landslide conditioning factors. Lithology, elevation, slope, aspect, curvature, distance from drainage, distance from road, land cover, and distance from lineament have been chosen as the landslide causative factors in the study area. This study presents a susceptibility mapping of landslides involving a combined bivariate statistical: frequency ratio (FR) and heuristic analytical hierarchy process (AHP) approach integrated in GIS environment. Validation and cross-validation of the susceptibility maps thus produced was achieved with the aid of landslide density approach in combination with prediction rate curve to check for the uniformity in the class areas in the susceptibility model produced. The analytical hierarchy process (AHP) produced results in which the lithology and slope factors had highest weights of 0.17 and 0.14 respectively. A strong correlation was observed in the lithology and slope conditioning factors; this is evident in the results of the FR approach with 10.68 and 6.86 FR values respectively. The landslide susceptibility maps were classified into five classes: very low susceptibility, low susceptibility, medium susceptibility, high susceptibility and very high susceptibility. Prediction rate curve was used to assess the predictive potential of the landslide susceptibility models, the result showed area under curve values of 70.49% for AHP and 72.09% for FR method. The similarity in the landslide density distribution in the susceptibility class, indicates a correlation between the generated susceptibility model and field observations. The statistical and heuristic methods employed have produced positive results; this was confirmed by the fact that all the 300 landslides were found to have occurred within the high susceptibility and very high susceptibility zones respectively.

Keywords

AHP GIS Landslide prediction Frequency ratio Susceptibility Validation 

Notes

Acknowledgments

The authors will like to thank the Department of Geology, University of Nigeria, Nsukka, for providing the enabling environment that facilitated this research.

Funding information

To my supervisor, lecturers of the department of geology, classmates, and my loving family for their financial and moral support, I say thank you.

References

  1. Amajor, L. C. (1987). Paleocurrent, petrography and provenance analyses of the Ajali Sandstone (Upper Cretaceous) southeastern Benue Trough, Nigeria. Sedimentary Geology, 54, 47–60.CrossRefGoogle Scholar
  2. Anbalagan, R., Kumar, R., Lakshmanan, K., Parida, S., & Neethu, S. (2015). Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim. Geoenvironmental Disasters, 2(1), 1–17.  https://doi.org/10.1186/s40677-014-0009-y.CrossRefGoogle Scholar
  3. 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), 15–31.  https://doi.org/10.1016/j.geomorph.2004.06.010.CrossRefGoogle Scholar
  4. 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 Perfecture, Japan. Landslides, 1, 73–81.CrossRefGoogle Scholar
  5. Banerjee, I. (1979). Analysis of cross-bedded sequences: an example from the Ajali Sandstone (Maastrichtian) of Nigeria. Quarterly Journal of  the Geological Mining and Metallurgical Society of India, 51, 69–81.Google Scholar
  6. Calligaris, C., Poretti, G., Tariq, S., & Melis, M. T. (2013). First steps towards a landslide inventory map of the Central Karakoram National Park. European Journal of Remote Sensing, 46(1), 272–287.CrossRefGoogle Scholar
  7. Chen, C. Y., & Huang, W. L. (2013). Land use change and landslide characteristics analysis for community-based disaster mitigation. Environmental Monitoring and Assessment, 185, 4125–4139.  https://doi.org/10.1007/s10661-012-2855-y.CrossRefGoogle Scholar
  8. Chen, X. L., Ran, H. L., & Yang, W. T. (2012). Evaluation of factors controlling large earthquake-induced landslides by the Wenchuan earthquake. Natural Hazards and Earth System Sciences, 12(12), 3645-3657.Google Scholar
  9. Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., & Pitilakis, K. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209–263.Google Scholar
  10. Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., et al. (2013). 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; Nat. Hazards, 65(1), 135–165.CrossRefGoogle Scholar
  11. Emeh, C., & Igwe, O. (2017). Variations in soils derived from an erodible sandstone formation and factors controlling their susceptibility to erosion and landslide. Journal of the Geological Society of India, 90(3), 362–370.CrossRefGoogle Scholar
  12. Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3), 99–111.  https://doi.org/10.1016/j.enggeo.2008.03.014.CrossRefGoogle Scholar
  13. Gupta, R. P., Kanungo, D. P., Arora, M. K., & Sarkar, S. (2008). Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. International Journal of Applied Earth Observation and Geoinformation, 10, 330–341.  https://doi.org/10.1016/j.jag.2008.01.003.CrossRefGoogle Scholar
  14. Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multiscale study, Central Italy. Geomorphology, 31(1), 181–216.CrossRefGoogle Scholar
  15. Hoque, M. (1977). Petrographic differentiation of tectonically controlled cretaceous sedimentary cycles, southeastern Nigeria. Sedimentary Geology, 17(3–4), 235–245.CrossRefGoogle Scholar
  16. Hoque, M., & Ezepue, M. C. (1977). Petrology and paleogeography of the Ajali Sandstone. Journal of Mining and Geology, 14, 16–22.Google Scholar
  17. Hutchinson, J. N. (1995). Landslide Hazard Assessment. Keynote Paper. In D. H. Bell (Ed.), Landslides, Proceeding of 6th International Symposium on Landslides, Christchurch (Vol. 1, pp. 1805–1841). Rotterdam: Balkema.Google Scholar
  18. Igwe, O. (2013). ICL/IPL activities in West Africa: landslide risk assessment and hazard mapping approach. Landslides, 10, 515–521.CrossRefGoogle Scholar
  19. Igwe, O. (2014). The compressibility and shear characteristics of soils associated with landslides in geologically different localities—case examples from Nigeria. Arabian Journal of Geosciences.  https://doi.org/10.1007/s12517-014-1616-3.CrossRefGoogle Scholar
  20. Igwe, O. (2015). The geotechnical characteristics of landslides on the sedimentary and metamorphic terrains of South-East Nigeria, West Africa. Geoenvironmental Disasters.  https://doi.org/10.1186/s40677-014-0008-z.
  21. Igwe, O. (2017). The hydrogeological attributes and mechanisms of a receding sedimentary terrain in the Anambra Basin, Southern Nigeria. Environmental Earth Sciences, 76(5), 196.CrossRefGoogle Scholar
  22. Igwe, O., & Fukuoka, H. (2014). The effect of water-saturation on the stability of problematic slopes at the Iva Valley area, Southeast Nigeria. Arabian Journal of Geosciences.  https://doi.org/10.1007/s12517-014-1398-7.CrossRefGoogle Scholar
  23. Igwe, O., Mode, W., Nnebedum, O., Okonkwo, I., & Oha, I. (2013). The analysis of rainfall-induced slope failures at Iva Valley area of Enugu State Nigeria. Environmental Earth Sciences.  https://doi.org/10.1007/s12665-013-2647-x.CrossRefGoogle Scholar
  24. Karsli, F., Atasoy, M., Yalcin, A., Reis, S., Demir, O., & Gokceoglu, C. (2009). Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey). Environmental Monitoring and Assessment, 156, 241–255.  https://doi.org/10.1007/s10661-008-0481-5.CrossRefGoogle Scholar
  25. Kayastha, P., et al. (2013). GIS based landslide susceptibility mapping using a fuzzy logic approach: a case study from Ghurmi-Dhad Khola area, Eastern Nepal. Journal of the Geological Society of India, 82(3), 249.CrossRefGoogle Scholar
  26. Kogbe, C. A. (1989). Palaeogeographic history of Nigeria from Albian times. In C. A. Kogbe (Ed.), Geology of Nigeria (pp. 257–275). Lagos: Elizabethan Publ. Co.Google Scholar
  27. Kumar, R., & Anbalagan, R. (2015). Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS. Journal of Earth System Science, 124(2), 431–448.CrossRefGoogle Scholar
  28. Ladipo, K. O. (1985). Tidal shelf depositional model for the Ajali Sandstone, Anambra Basin, southeastern Nigeria. Journal of African Earth Sciences, 5, 177–185.CrossRefGoogle Scholar
  29. Lee, S., & Pradhan, B. (2006). Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science, 115(6), 661-672.Google Scholar
  30. Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847–855.CrossRefGoogle Scholar
  31. Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.  https://doi.org/10.1007/s00254-005-1228-z.CrossRefGoogle Scholar
  32. Mathew, J., Jha, V. K., & Rawat, G. S. (2007). Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Current Science, 92(5), 628–638.Google Scholar
  33. Nwajide, C. S. (2013). Geology of Nigeria’s sedimentary basins (p. 548). Lagos: CSS Press.Google Scholar
  34. Pineda, M. C., Viloria, J., & Martínez-Casasnovas, J. A. (2016). Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region. Environmental Monitoring and Assessment, 188, 255.  https://doi.org/10.1007/s10661-016-5240-4.CrossRefGoogle Scholar
  35. Pirasteh, S., & Li, J. (2017). Landslides investigations from geoinformatics perspective: quality, challenges, and recommendations.Google Scholar
  36. Pradhan, B. (2010). Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems, 3(3), 370–381.CrossRefGoogle Scholar
  37. Pradhan, B., & Lee, S. (2010). Regional landslide susceptibility analysis using backpropagation neural network model at Cameron highland, Malaysia. Landslides, 7(1), 13–30.CrossRefGoogle Scholar
  38. Rai, P. K., Mohan, K., & Kumra, V. K. (2014). Landslide hazard and its mapping using remote sensing and GIS. Journal of Scientific Research, 58, 1–13.Google Scholar
  39. Rao, V. R., & Kumaran, K. P. N. (1981). Early angiosperm remains from the Ajali Sandstone, southeastern Nigeria. Journal of Mining and Geology, 18, 172–174.Google Scholar
  40. Rasyid, A. R., Bhandary, N. P., & Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3(1), 19.CrossRefGoogle Scholar
  41. Rawat, M. S., Uniyal, D. P., Dobhal, R., Joshi, V., Rawat, B. S., Bartwal, A., & Aswal, A. (2015). Study of landslide hazard zonation in Mandakini Valley, Rudraprayag district, Uttarakhand using remote sensing and GIS. Current Science, 109(1), 158–170.Google Scholar
  42. Reyment, R. A. (1965). Aspects of the geology of Nigeria: the stratigraphy of the cretaceous and cenozoic deposits (p. 145). Ibadan: Ibadan University Press.Google Scholar
  43. Rozos, D., Bathrellos, G. D., & Skilodimou, H. D. (2011). Comparison of the implementation of rock engineering system and analytical hierarchy process methods, based on landslide susceptibility maps, compiled in GIS environment. A case study from eastern Achaia County of Peloponnesus, Greece. Environment and Earth Science, 63(1), 49–63.CrossRefGoogle Scholar
  44. Saaty, T.L. (1980). The analytical hierarchy process. Priority Setting. MacGraw-Hill: Resource Allocation, New York International Book Company 287.Google Scholar
  45. Saha, A. K., Gupta, R. P., & Arora, M. K. (2002). GIS-based landslide hazard zonation in the Bhagirathi (ganga) valley, Himalayas. International Journal of Remote Sensing, 23(2), 357–369.CrossRefGoogle Scholar
  46. Sharma, S., & Mahajan, A. K. (2018). A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bulletin of Engineering Geology and the Environment, 1–18.  https://doi.org/10.1007/s10064-018-1259-9.CrossRefGoogle Scholar
  47. Swati, S., et al. (2018). Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India. Geoenvironmental Disasters, 5, 4.  https://doi.org/10.1186/s40677-018-0097-1.CrossRefGoogle Scholar
  48. Tofani, V., Raspini, F., Catani, F., & Casagli, N. (2013). Persistent Scatterer interferometry (PSI) technique for landslide characterization and monitoring. Remote Sensing, 5(3), 1045–1065.CrossRefGoogle Scholar
  49. Triantaphyllou, E., Lootsma, F. A., Pardalos, P. M., & Mann, S. H. (1994). On the evaluation and application of different scales for quantifying pairwise comparisons in fuzzy sets. Multi-Criteria Decision Analysis, 3, 1–23.CrossRefGoogle Scholar
  50. Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard and vulnerability assessment: an overview. Engineering Geology, 102, 3–4.Google Scholar
  51. Varnes, D. J. (1984). Landslide hazard zonation: a review of principles and practice. Commission on landslides of the IAEG, UNESCO. Natural Hazards, 3, 61.Google Scholar
  52. Yalcin, A., Reis, S., Aydinoglu, A. C., & Yomralioglu, T. (2011). A GIS based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistic regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3), 274–287.  https://doi.org/10.1016/j.catena.2011.01.014.CrossRefGoogle Scholar
  53. Yin, K.L., & Yan, T.Z. (1988) Statistical prediction model for slope instability of metamorphosed rocks. In proceedings of the 5th international symposium on landslides, Lausanne, Switzerland. Vol. 2, 1269–1272. The Netherlands: AA Balkema Rotterdam.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of GeologyUniversity of NigeriaNsukkaNigeria

Personalised recommendations