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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Abstract	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.
AbstractList	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. 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 M[sub.w] 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 (M[sub.w] 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.
Audience	Academic
Author	Pradhan, Biswajeet Ghamisi, Pedram Shanableh, Abdallah Al-Ruzouq, Rami Ghorbanzadeh, Omid Jena, Ratiranjan Khalil, Mohamad Ali Gibril, Mohamed Barakat A. Ganapathy, Ganapathy Pattukandan
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SubjectTerms	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
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