Abstract:
The flood is one of the frequently occurring
natural hazards within the sub-basin of Lake Tana. The
flood hazard within the sub-basin of Lake Tana causes
damage to cropland, properties, and a fatality every
season. Therefore, flood susceptibility modeling in this
area is significant for hazard reduction and management
purposes. Thus, the analytical hierarchy process (AHP),
bivariate (information value [IV] and frequency ratio
[FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field
survey, historical document, and Google Earth Imagery,
1,404-flood locations were determined, classified into
70% training datasets and 30% testing flood datasets
using a subset within the geographic information system
(GIS) environment. The statistical relationship between
the probability of flood occurrence and 11 flood-driving
factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by
summing all weighted aspects using a raster calculator.
It is classified into very low, low, moderate, high, and
very high susceptibility classes using the natural breaks
method. The accuracy and performance of the models
were evaluated using the area under the curve (AUC).
As the result indicated, the FR model has better performance
(AUC = 99.1%) compared to the AHP model (AUC = 86.9%),
LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This
