Explainable Artificial Intelligence (XAI) Model for Earthquake Spatial Probability Assessment in Arabian Peninsula

Among all the natural hazards, earthquake prediction is an arduous task. Although many studies have been published on earthquake hazard assessment (EHA), very few have been published on the use of artificial intelligence (AI) in spatial probability assessment (SPA). There is a great deal of complexi...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Jg. 15; H. 9; S. 2248
Hauptverfasser: Jena, Ratiranjan, Shanableh, Abdallah, Al-Ruzouq, Rami, Pradhan, Biswajeet, Gibril, Mohamed Barakat A., Khalil, Mohamad Ali, Ghorbanzadeh, Omid, Ganapathy, Ganapathy Pattukandan, Ghamisi, Pedram
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 24.04.2023
Schlagworte:
Accuracy
Algorithms
Arabian peninsula
Artificial intelligence
Comparative analysis
Deep learning
Digital Elevation Models
Digital imaging
Earthquake prediction
earthquake spatial probability assessment
Earthquakes
Explainable artificial intelligence
Fault lines
Geological hazards
Geological surveys
geophysics
GIS
Hazard assessment
hazard characterization
hybrids
inception v3-XGBoost
inventories
Landsat
Landsat satellites
Machine learning
Model accuracy
Modelling
Neural networks
prediction
Prediction models
Probability
probability analysis
Remote sensing
Satellite imagery
Seismic activity
Seismic hazard
Seismology
Shaking
Tectonics
Turkey (country)
United States Geological Survey
XAI
United Arab Emirates
Saudi Arabia
Arabian Peninsula
Iran
Gulf of Aden
Red Sea
Gulf of Aqaba
Indonesia
Dead Sea
ISSN:2072-4292, 2072-4292
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Zusammenfassung:Among all the natural hazards, earthquake prediction is an arduous task. Although many studies have been published on earthquake hazard assessment (EHA), very few have been published on the use of artificial intelligence (AI) in spatial probability assessment (SPA). There is a great deal of complexity observed in the SPA modeling process due to the involvement of seismological to geophysical factors. Recent studies have shown that the insertion of certain integrated factors such as ground shaking, seismic gap, and tectonic contacts in the AI model improves accuracy to a great extent. Because of the black-box nature of AI models, this paper explores the use of an explainable artificial intelligence (XAI) model in SPA. This study aims to develop a hybrid Inception v3-ensemble extreme gradient boosting (XGBoost) model and shapely additive explanations (SHAP). The model would efficiently interpret and recognize factors’ behavior and their weighted contribution. The work explains the specific factors responsible for and their importance in SPA. The earthquake inventory data were collected from the US Geological Survey (USGS) for the past 22 years ranging the magnitudes from 5 Mw and above. Landsat-8 satellite imagery and digital elevation model (DEM) data were also incorporated in the analysis. Results revealed that the SHAP outputs align with the hybrid Inception v3-XGBoost model (87.9% accuracy) explanations, thus indicating the necessity to add new factors such as seismic gaps and tectonic contacts, where the absence of these factors makes the prediction model performs poorly. According to SHAP interpretations, peak ground accelerations (PGA), magnitude variation, seismic gap, and epicenter density are the most critical factors for SPA. The recent Turkey earthquakes (Mw 7.8, 7.5, and 6.7) due to the active east Anatolian fault validate the obtained AI-based earthquake SPA results. The conclusions drawn from the explainable algorithm depicted the importance of relevant, irrelevant, and new futuristic factors in AI-based SPA modeling.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15092248