The study of earthquake activity and seismic hazard assessment of Egypt is very important due to the great and rapid spreading of large investments in national projects, especially the nuclear power plant that will be held in the northern part of Egypt. Although Egypt is characterized by low seismicity, it has experienced occurring of damaging earthquake effect through its history. The seismotectonic sitting of Egypt suggests that large earthquakes are possible particularly along the Gulf of Aqaba–Dead Sea transform, the Subduction zone along the Hellenic and Cyprean Arcs, and the Northern Red Sea triple junction point. In addition some inland significant sources at Aswan, Dahshour, and Cairo-Suez District should be considered. The seismic hazard for Egypt is calculated utilizing a probabilistic approach (for a grid of 0.5°×0.5°) within a logic-tree framework. Alternative seismogenic models and ground motion scaling relationships are selected to account for the epistemic uncertainty. Seismic hazard values on rock were calculated to create contour maps for four ground motion spectral periods and for different return periods. In addition, the uniform hazard spectra for rock sites for different 25 periods, and the probabilistic hazard curves for Cairo, and Alexandria cities are graphed. The peak ground acceleration (PGA) values were found close to the Gulf of Aqaba and it was about 220gal for 475year return period. While the lowest (PGA) values were detected in the western part of the western desert and it is less than 25gal

The 1992 Cairo earthquake, with a moment magnitude of 5.8, is the most catastrophic earthquake to shock the Greater Cairo (GC) in recent decades. According to the very limited number of seismological stations at that time, the peak ground acceleration (PGA) caused by this event was not recorded. PGA calculation requires identification of nature of the earthquake source, the geologic setting of the path between the source and site under investigation and soil dynamic properties of the site. Soil dynamic properties are acquired by geotechnical investigations and/or geophysical survey. These two methods are costly and are not applicable in regional studies. This study presents an adaptive and reliable PGA prediction model using machine learning (ML) along with six standard geographic information system (GIS) interpolation methods (IDW, Kriging, Natural, Spline, TopoToR, and Trend) to predict the spatial distribution of PGA caused by this event over the GC. The model is employed to estimate the exposure of the urban area and population in the GC based on the available geotechnical and geophysical investigations. The exposure (population) data is from free and easy-access sources, e.g., Landsat images and the Global Human Settlement Population Grid (GHS-POP). The results show that Natural, Spline, and Trend are not suitable GIS interpolation techniques for generating seismic hazard maps (SHMs), while the Kriging Method shows sufficient prediction. Interestingly, with an accuracy of 96%, the ML model outperforms the classical GIS methodologies.