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Flooding is among the most catastrophic and common natural events. It not only endangers human lives, their livelihoods, and possessions but also devastates the nation's economy. Increased flooding is an inevitable consequence of climate change. Hence, Identification of flood suspectable hotspots is vital for flood risk management along with disaster handling. The primary objective of this research is to use a frequency ratio model to classify flood-prone zones in two provinces of Pakistan. The flood inventory map was developed using 230 flood location points in Northern Sindh and Southern Punjab. Aspect, profile curvature, elevation, slope, normalized difference vegetation index (NDVI), normalized difference soil index (NDSI), distance from the road, distance from the river, land use/land cover (LULC) and rainfall were among the ten (10) determining factors. The data were randomly divided into two distinct datasets, with 70% flood points (161) used for inventory formulation and the other 30% (69 flood points) for result validation. The flood vulnerability map was categorized into five different zones ranging from very low (19.73%) to very high (20.37%) susceptibility range. The area under the receiver operating characteristic curve (ROC) and area under curve (AUC) was used to demonstrate the prediction result that yielded a reasonable score of 77.4%. The study suggested that in comparison to other studied districts, Jacobabad is the most prone region with acute vulnerability and constrained resilience. The presented data can serve as a source for tracking, assessing, and predicting potential flood activity in the area and could be beneficial for planners and decision-makers involved in early disaster response planning within the country.
Munir et al. (Thu,) studied this question.