###### Abstract
These models can be used to determine the connections between the location of an FSM-appropriate or inappropriate area and its conditioning elements. Maps of landslides, groundwater, and floods have all been successfully created using FR, RF, and KNN, as well as some further innovative data mining techniques (e.g., [18; 19; 20; 21; 22; 23; 24]). The FR model is easier to understand and provides more details on the connections between the conditions for FSM and the area's suitability for FSM [25; 26]. In Egypt, rainfall caused flash floods frequently during 1994, 2010, and 2016 [27; 28; 29]. Many Egyptian researchers have recorded and studied flash floods in some of Egypt's urbanized regions [16; 30; 31; 32; 33; 34]. In the northern part of the country in October 2016, 73 individuals were killed, and 30 were injured [35; 36; 37]. Additionally, numerous floods devastated the city of New Cairo in the years 2018 and 2019 due to, as reported by the Egyptian Meteorological Authority, an increase in the rate of rainfall, displacing a sizable number of citizens, destroying property, costing a sizable amount of money, and taking the lives of seven people [38; 39; 40]. At least six and twenty-five fatalities occurred in Alexandria and Beheria, respectively, in 2015. Therefore, an early flash flood warning is required to control and lessen the potential impact of such disasters. The significance of this study lies in applying recent statistical models to create a map of flood susceptibility to predict which urban areas may experience flash floods. The outcomes offer a trustworthy addition to the detection and evaluation of flash floods. New Cairo City is one of the most prestigious cities in Egypt. Meanwhile, it was founded in 2000 to alleviate the overcrowding in Cairo City and boasts an area of 267 km\({}^{2}\). It is one of Egypt's most attractive and populous cities since it contains six international universities and many economical and luxurious residential buildings. Furthermore, the region of New Cairo city has been witnessing periodic devastating flash floods every year. Therefore, this study sheds light on the surface runoff process and its various risks by using an applied scientific method to study and analyze the runoff risks and conduct a hydrological analysis to identify the areas affected by heavy rains. The importance of this study stems from the fact that it discusses one of the most important risks that threaten urban communities in general and in the study area in particular. This risk affects the security and stability of the population and their property. The dangers of runoff can be studied through the application of geospatial techniques and ArcGIS geographic information systems software in order to support the decision maker in making development decisions to develop the region and similar areas in a way that preserves the lives of its inhabitants. Remote sensing (RS) and ArcGIS 10.8.1 software tools have become increasingly popular in recent years because they offer a fresh perspective on vulnerability assessments with the necessary justification. As it provides faultless information about a place, the analysis of satellite imageries on the RS and GIS platforms produces good findings for flood susceptibility and vulnerability mapping [41]. The environment it generates is incredibly pleasant, allowing a wide range of models to operate and alter data to assess flood risk, and the outcomes become more plausible and acceptable. The application of RS and GIS approaches for assessing flood susceptibility is currently widespread and very successful [1]. There are numerous models and methods that can be employed in place of one another for mapping flood vulnerabilities. One of the widely acceptable and extremely accurate strategies for hazard assessment is the frequency ratio (FR) model [42]. One type of bivariate statistical analysis (BSA) is frequency ratio (FR), which assigns values to each class of each parameter and assesses its influence on flood occurrence [43; 44].