Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo

Omar Hamdy, Hanan Gaber, Mohamed S. Abdalzaher () and Mahmoud Elhadidy
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Omar Hamdy: Architectural Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Hanan Gaber: National Data Centre, National Research Institute of Astronomy and Geophysics (NRIAG), Cairo 11421, Egypt
Mohamed S. Abdalzaher: Seismology Department, National Research Institute of Astronomy and Geophysics (NRIAG), Cairo 11421, Egypt
Mahmoud Elhadidy: Seismology Department, National Research Institute of Astronomy and Geophysics (NRIAG), Cairo 11421, Egypt

Sustainability, 2022, vol. 14, issue 17, 1-24

Abstract: 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.

Keywords: urban area; seismic hazard; machine learning; GIS; Greater Cairo (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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