Predicting Landslide Susceptibility Using Cost Function in Low-Relief Areas: A Case Study of the Urban Municipality of Attecoube (Abidjan, Ivory Coast)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by
geographic information systems (GIS) can help mitigate potential losses and enhance
disaster resilience. This study evaluates landslide susceptibility using logistic regression
and frequency ratio models. The analysis is based on a dataset comprising 54 mapped
landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor
variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and
an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived
from multiple data sources, supports the spatial analysis. The landslide inventory was
randomly divided into two subsets: 80% for model calibration and 20% for validation.
After optimization and statistical testing, the selected thematic layers were integrated to
produce a susceptibility map. The results indicate that 6.3% (0.7 km2) of the study area is
classified as very highly susceptible. The proportion of the sample (61.2%) in this class had
a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE,
S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model
performance was assessed using the area under the receiver operating characteristic curve
(AUC), demonstrating strong predictive capability. These findings can support informed
land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in making decisions and implementing protective measures.