Spatial Analysis of Flood Susceptibility Using Frequency Ratio in Shigar Valley, North Pakistan

Flash floods are sudden, large amounts of water that happen in a short amount of time. They are usually caused by heavy rain, storms, or sudden melting. The frequency ratio (FR) model is used in this study to make maps showing how likely it is that flash floods will happen in the Shigar Valley in north Pakistan. The primary aim of this study is to identify the elements that influence flash floods and to develop a zonation based on flash flood vulnerability within the designated study region. The Global Position System (GPS) was used to determine the locations of 428 flash floods to establish targets and conduct a field study. A flood danger inventory map was developed using Geographic Information System (ArcGIS 10.5) software, based on the specific locations of flash floods. The sites of flash flood hazards were randomly allocated into training and validation datasets. The models were constructed using 70% (300) of the data, which pertained to the sites of flash flood threats. The remaining 30% (128) of flash flood locations were allocated for model testing purposes. The present study included a set of 11 conditioning parameters, including slope, elevation, aspect, curvature, and land use/land cover, etc. In addition, the effects of each conditioning parameter are evaluated by testing all values. The model has success and accuracy rates of 79.41% and 77.09%, respectively. This outcome is classified as a good category. Based on the findings, it can be concluded that the frequency ratio model is appropriate for the zonation of flash flood susceptibility. The results indicate that low-elevation and flat regions are the most susceptible locations, necessitating land use planning, and infrastructure enhancements to effectively mitigate flash flood dangers. This research utilize frequency ratio methods to conduct comprehensive flood susceptibility studies in Shigar Valley. This study offers valuable perspectives on integrating advanced spatial analytic techniques into resilient urban design for the development of sustainable cognitive cities.