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.
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The authors will like to thank the Department of Geology, University of Nigeria, Nsukka, for providing the enabling environment that facilitated this research.
To my supervisor, lecturers of the department of geology, classmates, and my loving family for their financial and moral support, I say thank you.
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Ozioko, O.H., Igwe, O. GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs Southeast Nigeria. Environ Monit Assess 192, 119 (2020). https://doi.org/10.1007/s10661-019-7951-9
- Landslide prediction
- Frequency ratio