Abstract
A clear understanding of the spatial distribution of earthquake events facilitates the prediction of seismicity and vulnerability among researchers in the social, physical, environmental, and demographic aspects. Generally, there are few studies on seismic risk assessment in United Arab Emirates (UAE) within the geographic information system (GIS) platform. Former researches and recent news events have demonstrated that the eastern part of the country experiences jolts of 3‐5 magnitude, specifically near Fujairah city and surrounding towns. This study builds on previous research on the seismic hazard that extracted the eastern part of the UAE as the most hazard‐prone zone. Therefore, this study develops an integrated analytical hierarchical process (AHP) and machine learning (ML) for risk mapping considering eight geospatial parameters—distance from shoreline, schools, hospitals, roads, residences, streams, confined area, and confined area slope. Experts’ opinions and literature reviews were the basis of the AHP ranking and weighting system. To validate the AHP system, support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were applied to the datasets. The datasets were split into 60 : 40 ratio for training and testing. Results show that SVM has the highest accuracy of 79.6% compared to DT and RF with a “predicted high” precision of 87.5% attained from the model. Risk maps from both AHP and ML approaches were developed and compared. Risk analysis was categorised into 5 classes “very high,” “high,” “moderate,” “low,” and “very low.” Both approaches modelled relatable spatial patterns as risk‐prone zones. AHP approach concluded 3.6% as “very high” risk zone, whereas only 0.3% of total area was identified from ML. The total area for the “very high” (20 km2) and “high” (114 km2) risk was estimated from ML approach.
Highlights
Earthquakes are considered short-term calamities that exert a significant long-term impact on human lives, infrastructure, and the economy that can last for decades or longer [1, 2]
The risk map is developed using analytical hierarchical process (AHP) and the weighted overlay, and machine learning (ML) techniques have been employed to understand the parameters for the risk associated with earthquake hazard
Though both the maps depicted a similar pattern for adversity, marginal differences in each class area were estimated, refer to Table 7
Summary
Earthquakes are considered short-term calamities that exert a significant long-term impact on human lives, infrastructure, and the economy that can last for decades or longer [1, 2]. The severity of a tremor can range from light, i.e., nearly no impact, to sufficiently strong to destroy means of livelihood [3]. The seismic actions are measured based on an earthquake’s frequency and magnitude occurring within a certain period. These hazards contribute to severe vulnerability in terms of loss of human life, society, and economy. The vulnerability of built-up areas to earthquakes and other natural hazards is a consequence of construction methodology and the quality of materials [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
