| dc.description.abstract | Floods are one of the most prevalent and devastating natural disasters, resulting in immeasurable damage to natural and man-made features and its occurrence cannot be prevented and as such measures to minimize the arising negative consequences should be put in place. Effective mitigation against floods along the Albert Nile in Moyo district has however been hindered by the absence of updated information about the spatial variability of susceptibility yet this information is critical for relocation and physical planning. This research was aimed at determining the spatial variability of flood susceptibility along Albert Nile in Moyo district to bridge this information gap. A flood inventory was developed using Sentinel 1 SAR imagery within GEE using preflood and post flood periods of August 2019 and 2020 respectively.70% (76) of flood locations were randomly selected for constructing the flood susceptibility map parameters and the rest of the 30% (29) flood locations were used for justifying the outcomes. Site specific flood conditioning factors like elevation, slope, NDVI, proximity to river, TWI and rainfall were endorsed for understanding the flood mechanism. The frequency ratio (FR) statistical method was applied to characterize the relationship between the past flood event and flood conditioning factors and as well model the flood susceptibility. Results showed that distance from the river was the most influential followed by TWI, elevation and rainfall respectively. The NDVI and slope were the least influential. The findings showed that the locations with varying levels of flood susceptibility were determined from the susceptibility map obtained using the frequency ratio model. Approximately 22.7% and 20.5% were classified with very high and high susceptibility respectively and the remaining 56.8% of the study area was covered by moderate, low and very low susceptibility. The Gbalala and Pakoma villages had the highest risk factor. The other smaller villages of Edie, Logubu South, Olia, Pajaru, Pachoro, Pachoro, Patere and Khidi also posed a high-risk factor since the level of susceptibility depended on many factors, size of village being one of them. The results were validated using the ROC giving an AUC of 0.881275 corresponding to prediction accuracy of 88.1275% which shows the results are reliable. The obtained results provided vital findings which will help managers to optimally allocate flood management resources to efficiently mitigate against floods within Laropi sub county in Moyo District. Several additional flood-conditioning parameters, such as land use land cover and drainage density could be used in future studies in this study area to gain more comprehensive evidence and flood-related information. Authorities should ensure that the 100m buffer from river banks is observed so as to mitigate future flood. | en_US |
