Landslide susceptibility mapping using weights of evidence model on the slopes of Mount Elgon, eastern Uganda
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Generally, landslide susceptibility mapping is an important step in mitigating their impacts. There is, however, particular dearth of information on the application of GIS-based bivariate methods particularly the weights of evidence model in mapping landslide susceptibility on the slopes of Mount Elgon in eastern Uganda. This study, therefore, evaluated the susceptibility of Bukalasi milli-watershed to landslides, as an early warning strategy for the major landslide hotspot in Uganda. A landslide inventory for the study area was prepared, and the weights of influence of selected landslide-conditioning factors were calculated to present their relative importance in landslide susceptibility. Eight conditioning factors were considered in this study namely; land use, lithology, rainfall, elevation, slope aspect, slope angle, plan curvature and profile curvature. Following the results of the Agterberg-Cheng conditional independence test (probability = 62.5%), the hypothesis of conditional independence among these factors was accepted. Validation using the ROC indicated satisfactory performance of the model considering the model prediction rate (Area under the Curve = 0.882) and success rate (Area under the Curve = 0.912). The final landslide susceptibility map highlights high susceptibility in the southern and western parts of the study area. It further shows that whereas Bukibumbi, Bundesi and Suume parishes are the most prone parishes, Shibanga Parish is relatively the least prone to landslides disasters. Thus, such highly susceptible areas should be prioritised during intervention programmes, especially relocation of the residents at risk. Since the absence of forests has been indicated to exacerbate susceptibility to landslides, deforestation should have severe penalties, and extensive tree-planting should instead be encouraged. Other human activities like farming on fragile slopes, which would further destabilise the slopes should particularly be discouraged.
- Natural Sciences