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Scientists develop AI model to predict earthquakes in Arabian Peninsula

2024-01-30  |   Editor : houxue2018  
Category : News

Abstract

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.

Content

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 seismic profiles.

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.

Sources:

UNDRR

https://www.preventionweb.net/collections/artificial-intelligence-disaster-risk-reduction .

Provided by the IKCEST Disaster Risk Reduction Knowledge Service System

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