Mapping urban flood susceptibility in Ouagadougou, Burkina Faso

• Published in: Environmental earth sciences 2024-10, Vol.83 (19), p.561, Article 561
• Main Authors: Traoré, Karim, Fowe, Tazen, Ouédraogo, Mathieu, Zorom, Malicki, Bologo/Traoré, Maïmouna, Toé, Patrice, Karambiri, Harouna
• Format: Article
• Language: English
• Subjects: Biogeosciences; Confidence intervals; Curvature; Datasets; Development strategies; Distance; Earth and Environmental Science; Earth Sciences; Effectiveness; Elevation; Environmental Science and Engineering; Flood control; Flood damage; Flood predictions; Flood prevention; Flooding; Floods; Geochemistry; Geology; Historic floods; Hydrology/Water Resources; Land cover; Land use; Normalized difference vegetative index; Original Article; Precipitation; Rainfall; Regression models; Rivers; Soil types; Statistical analysis; Susceptibility; Terrestrial Pollution; Topography; Urban development; Urbanization; Vegetation index; Wetness index
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-024-11871-0

Ouagadougou, the capital city of Burkina Faso, is facing significant economic and social damages due to recurring floods. This study aimed to develop a flood susceptibility map for Ouagadougou using a logistic regression (LR) model and 14 flood conditioning factors, including elevation, slope, aspect, profile curvature, plan curvature, topographic position index (TPI), topographic roughness index (TRI), flow direction, topographic wetness index (TWI), distance to river, rainfall, land use/land cover (LULC), normalized difference vegetation index (NDVI) and soil type. A historical flood inventory map was created from household survey data, identifying 1026 flooded sites which were divided into a training dataset (70%) and a validation dataset (30%). The factors that had a statistically significant influence (p-value  1.96) at the 95% confidence level were, in order of importance, elevation, distance to river, rainfall, plan curvature and NDVI. The receiver operating characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the city is very high susceptible to flooding, 18.99% has high susceptibility, 18.43% has moderate susceptibility, and 19.98% and 24.18% have low and very low susceptibility, respectively. This research provides valuable information for policy makers to develop effective flood prevention and urban development strategies.