Browsing by Author "Shirambere, Gervais"

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  • Publication
    Publication
    Comparative assessment of landslide susceptibility by logistic regression and first order second moment method: Case study of Bujumbura Peri-Urban Area, Burundi.
    (Journal of Engineering Research and Application, 2018-08-24) Shirambere, Gervais; Nyadawa, Maurice O.; Masekanya, Jean pierre; Nyomboi, Timothy
    Several landslides incidents in the Bujumbura region are reported regularly by independent sources. However, few studies on the causes in the region have been conducted and no record of susceptibility map at a regional exists. In this study, two different approaches are applied to map landslide susceptibility in the region. The physical approach is based on mohr-coulomb failure criterion and is applied using a probabilistic approach, the first order second moment method. The statistical approach is based on logistic regression. The study has two objectives: (i) to map landslide susceptibility in the region and (ii) to compare the results of the different approaches. Applying the two approaches in a GIS framework, two susceptibility map are produced. The accuracy of the two models is independently assessed using ROC and AUC curves. A comparative analysis of the results is conducted and the results shows a fair spatial correlation. The susceptibility maps are compared using rank differences and ArcSDM and a spatial comparison map of susceptibility levels is produced.
  • Publication
    Publication
    Probabilistic landslide risk assessment: Case study of Bujumbura
    (EDP Sciences, 2018-12-14) Shirambere, Gervais; Nyadawa, Maurice O.; Masekanya, Jean pierre; Nyomboi, Timothy
    A spatial probabilistic landslide risk assessment and mapping model has been applied in a data scare region. The probabilistic model is based on a physical model based on Mohr coulomb failure criterion. A Monte Carlo simulation technique is applied to field collected data. The results are integrated and a probability of landslide is obtained at each cell level. The results are compared to a prepared landslide inventory. The overall accuracy of the model is 79.69%.