FLOOD SUSCEPTIBILITY ASSESSMENT IN THE NAKAMBE BASIN, BURKINA FASO

Abstract

Flood is the most disastrous natural disaster in the world due to its devastating effects that endanger lives and cause property damage to the affected areas. Flood events are alarmingly increasing all over the world; and despite the recent efforts towards mitigation and management, vulnerability to flood damages is likely to continue to grow. This research mainly aims at identifying areas susceptible to flood through the application of two different models namely Frequency Ratio (FR) and Logistic Regression (LR) using remote sensing data (RS) and Geographical Information System (GIS). The study was conducted in the Nakambé Basin, Burkina Faso, and consisted of the use of thirteen predicting factors and 250 historical flood locations. The flood points were randomly split into 70% training and 30% validation through the Area Under Curve (AUC) method. The results indicated that the LR model performed best for the susceptibility mapping in the study area disclosing 93.1% of prediction rate compared to the FR model (80.6%). Moreover, the slope and elevation were found to be the most contributing factors in the study area. The outcome of this study can be used by the planners and policy makers to implement different management and mitigation measures with a view to the local environment.