Scientists developed a groundbreaking earthquake prediction
study using an AI model in the Arabian Peninsula. The model,
combining Inception v3-XGBoost and SHAP, achieved 87.9% accuracy,
a significant improvement over previous methods. It notably
predicted seismic activities, including the major Mw 7.8 earthquake
in Turkey. This advancement demonstrates AI's potential in
enhancing earthquake forecasting and suggests its application
in global seismic research.
This innovative study introduces a major advancement
in earthquake forecasting by leveraging artificial intelligence (AI)
technology, particularly focusing on the Arabian Peninsula.
The research team created a unique hybrid model combining
Inception v3-ensemble extreme gradient boosting (XGBoost)
with shapely additive explanations (SHAP). This approach
is unprecedented, marking the first use of AI in spatial
probability assessment (SPA) for earthquake prediction.
The essence of this study lies in its methodology and data
utilization. Researchers gathered extensive earthquake data
spanning 22 years from the US Geological Survey (USGS). They
also incorporated Landsat-8 satellite imagery and digital
elevation model (DEM) data to enrich their analysis. The
hybrid Inception v3-XGBoost model is a significant stride
forward in earthquake prediction, boasting an accuracy rate
of 87.9%. This model is unique in its ability to perform both
feature learning and prediction more effectively than standalone models.
A key insight from this study is the critical role of new
factors in earthquake prediction models. The inclusion of
seismic gaps and tectonic contacts has been shown to be
crucial for enhancing the accuracy of these models. This
finding is particularly relevant given the model's success
in accurately predicting seismic activity in areas like the
Gulf of Aden, Red Sea, Iran, and Turkey. The study’s
prediction capabilities were highlighted by its accurate
foresight regarding the significant Mw 7.8 earthquake in Turkey.
The research underscores the complexities and challenges
inherent in earthquake prediction. Nonetheless, it demonstrates
that AI can significantly enhance the accuracy and reliability
of such predictions. The study is not just a theoretical exercise
but has practical implications. The authors suggest that their
model could substantially contribute to the development of
seismic codes and inform construction practices in the Arabian
Peninsula and potentially other regions. This would involve
using the model's findings to determine whether retrofitting is
necessary to minimize ground-shaking effects in earthquake-prone areas.
Furthermore, the study opens up possibilities for global application
of this AI-driven model. While it has provided a robust and
effective approach to SPA within the context of the Arabian
Peninsula, its global applicability and effectiveness in diverse
geotectonic conditions remain to be tested with new factors.
This suggests an exciting frontier for future research in
earthquake prediction, where AI models could be adapted and
applied to various regions worldwide, each with their unique
In conclusion, this study represents a significant leap in
the field of earthquake prediction, demonstrating the powerful
role AI can play in enhancing our ability to forecast seismic
events. It holds the promise of not only advancing scientific
understanding but also providing practical solutions to
mitigate the risks associated with earthquakes in different
parts of the world.
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System