GIS-Based Weights-of-Evidence Model for Landslide Susceptibility Mapping and Assessment in the Hillslopes of Bujumbura, Burundi

Abstract

The aim of this research is the modelling of landslide susceptibility in the hillslopes of Bujumbura using the Weights-of-Evidence model, a probabilistic data modelling approach relevant for predicting future landslides at a regional scale. Initially, characteristics and spatial mapping of different landslides type were identified (fall, flow, slide, complex) by thorough interpretation of high-resolution remote sensing data (mountainous areas with difficult access) and intensive fieldwork. Subsequently, the main landslides controlling factors were selected (lithology, fault density, land use, drainage density, slope aspect, curvature, slope angle, and elevation) using in-depth field knowledge and relevant literature. A landslide inventory map with a total of 569 landslide sites was constructed using the data from various sources. Out of those 569 landslide sites, 285 (50.1%) of the data taken before the 2000s was used for training and the remaining 284 (49.9%) sites (post-2000 events) were used for the accuracy assessment purpose. Thereafter, a prediction map of future landslides was generated with an accuracy of 73.7%. The main geo-environmental landslides factors retained are the high density of drainage networks, the lithology often made with weathered gneiss, the high fault density, the steep topography and the convex slope curvature. The landslide susceptibility map validated was reclassified into very high, high, moderate, low and very low zones. The established susceptibility map will allow with the interaction of the real terrain to locate roads, dwellings, urban extension areas, dams located in high landslides risk zones. These infrastructures will require intervention to address their vulnerability with new facilities, slope stabilization, creation of bypass roads, etc. The susceptibility map produced will be a powerful decision-making tool for drawing up appropriate development plans. Such an approach will make it possible to mitigate the socio-economic impacts due to slope instabilities.