{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:46:44Z","timestamp":1769021204480,"version":"3.49.0"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, UAE through the Vice Chancellor Research Fund"},{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH, Vienna, Austria"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/rs15092248","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T06:20:42Z","timestamp":1682317242000},"page":"2248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Explainable Artificial Intelligence (XAI) Model for Earthquake Spatial Probability Assessment in Arabian Peninsula"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6180-4673","authenticated-orcid":false,"given":"Ratiranjan","family":"Jena","sequence":"first","affiliation":[{"name":"GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9808-4120","authenticated-orcid":false,"given":"Abdallah","family":"Shanableh","sequence":"additional","affiliation":[{"name":"GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates"},{"name":"Civil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7111-0061","authenticated-orcid":false,"given":"Rami","family":"Al-Ruzouq","sequence":"additional","affiliation":[{"name":"GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates"},{"name":"Civil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6465-6231","authenticated-orcid":false,"given":"Mohamed Barakat A.","family":"Gibril","sequence":"additional","affiliation":[{"name":"GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates"},{"name":"Civil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3338-0092","authenticated-orcid":false,"given":"Mohamad Ali","family":"Khalil","sequence":"additional","affiliation":[{"name":"GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-8770","authenticated-orcid":false,"given":"Omid","family":"Ghorbanzadeh","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Landstra\u00dfer Hauptstra\u00dfe 5, 1030 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3356-6508","authenticated-orcid":false,"given":"Ganapathy Pattukandan","family":"Ganapathy","sequence":"additional","affiliation":[{"name":"Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"MachineLearning Group, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Str. 40, 09599 Freiberg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102631","DOI":"10.1016\/j.ijdrr.2021.102631","article-title":"Extension of FEMA and SMUG Models with Bayesian Best-Worst Method for Disaster Risk Reduction","volume":"66","author":"Yanilmaz","year":"2021","journal-title":"Int. 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