Ratiranjan Jena, Abdallah Shanableh, Rami Al-Ruzouq, Biswajeet Pradhan, Mohamed Barakat A Gibril, Mohamad Ali Khalil, Omid Ghorbanzadeh, and Pedram Ghamisi

Method overview.

Model architectures and data flow for earthquake hazard assessment.

Earthquakes are the most destructive natural hazards because of their adversely severe impacts on urban areas. Earthquakes affect people’s lives and properties, thus captivating the extensive attention of seismologists. Carrying out probability and hazard assessment for the prevention, and reduction of mega-events and recovery will be of great significance in affected areas. Given that limited studies have attempted to estimate earthquake Spatial Probability Assessment (SPA) in the Arabian Peninsula, this study aims to evaluate the SPA and Earthquake Hazard Assessment (EHA). This study implements and evaluates various machine learning and explainable-AI (XAI) techniques for the estimation of SPA and EHA in the Arabian Peninsula, explores the contribution and highlights the importance of different factors in the development of AI-based models. A total of twelve factors ranging from seismological to geophysical factors were evaluated. Two machine learning models namely Light Gradient Boosting Machine (LightGBM) and deep Recurrent Neural Networks (RNN) along with three XAI approaches (i.e, Smart predictor, Smart Explainer and Local Interpretable Model-Agnostic Explanation (LIME) model) were investigated. Results of the comparative earthquake SPA estimation demonstrated that the accuracy of 89% and 87% were achieved by LightGBM and RNN models. Moreover, the results of the XAI models show that the Smart Predictor provides better spatial outputs than the other evaluated XAI models. The stable factors identified by Smart Predictor were magnitude variation and earthquake frequency whereas the important factors were magnitude variation, earthquake frequency, depth variation, and seismic gap. Collectively, results of SPA show that, the Gulf of Aden, Red Sea, Iran, and Turkey are falling under a very-high SPA index (0.991–1). Correspondingly, Gulf areas, coastal areas of Saudi Arabia, and areas in the Zagros fault and Anatolian fault zone fall under a very-high hazard zone. This research could support planners, and decision-makers for emergency planning, infrastructure development, and reconstruction projects.

Remote Sensing Applications: Society and Environment, 31, 101004, 2023-06-01.

View paper
IARAI Authors
Omid Ghorbanzadeh, Dr. Pedram Ghamisi
Earth Observation
Earthquake, Explainable Artificial Intelligence, Machine Learning


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