{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T17:55:16Z","timestamp":1776794116604,"version":"3.51.2"},"reference-count":64,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,26]],"date-time":"2021-09-26T00:00:00Z","timestamp":1632614400000},"content-version":"vor","delay-in-days":268,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>A clear understanding of the spatial distribution of earthquake events facilitates the prediction of seismicity and vulnerability among researchers in the social, physical, environmental, and demographic aspects. Generally, there are few studies on seismic risk assessment in United Arab Emirates (UAE) within the geographic information system (GIS) platform. Former researches and recent news events have demonstrated that the eastern part of the country experiences jolts of 3\u20105 magnitude, specifically near Fujairah city and surrounding towns. This study builds on previous research on the seismic hazard that extracted the eastern part of the UAE as the most hazard\u2010prone zone. Therefore, this study develops an integrated analytical hierarchical process (AHP) and machine learning (ML) for risk mapping considering eight geospatial parameters\u2014distance from shoreline, schools, hospitals, roads, residences, streams, confined area, and confined area slope. Experts\u2019 opinions and literature reviews were the basis of the AHP ranking and weighting system. To validate the AHP system, support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were applied to the datasets. The datasets were split into 60\u2009:\u200940 ratio for training and testing. Results show that SVM has the highest accuracy of 79.6% compared to DT and RF with a \u201cpredicted high\u201d precision of 87.5% attained from the model. Risk maps from both AHP and ML approaches were developed and compared. Risk analysis was categorised into 5 classes \u201cvery high,\u201d \u201chigh,\u201d \u201cmoderate,\u201d \u201clow,\u201d and \u201cvery low.\u201d Both approaches modelled relatable spatial patterns as risk\u2010prone zones. AHP approach concluded 3.6% as \u201cvery high\u201d risk zone, whereas only 0.3% of total area was identified from ML. The total area for the \u201cvery high\u201d (20\u2009km<jats:sup>2<\/jats:sup>) and \u201chigh\u201d (114\u2009km<jats:sup>2<\/jats:sup>) risk was estimated from ML approach.<\/jats:p>","DOI":"10.1155\/2021\/6638316","type":"journal-article","created":{"date-parts":[[2021,9,26]],"date-time":"2021-09-26T20:08:10Z","timestamp":1632686890000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE"],"prefix":"10.1155","volume":"2021","author":[{"given":"Diena","family":"Al-Dogom","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7111-0061","authenticated-orcid":false,"given":"Rami","family":"Al-Ruzouq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-3463","authenticated-orcid":false,"given":"Bahareh","family":"Kalantar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen","family":"Schuckman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed","family":"Al-Mansoori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunanda","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hussain","family":"Al-Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naonori","family":"Ueda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,9,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-013-0974-6"},{"key":"e_1_2_9_2_2","first-page":"65","volume-title":"Advanced ICTs for disaster management and threat detection: collaborative and distributed frameworks","author":"Duncan A. 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