{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:40:03Z","timestamp":1768416003823,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE01277002"],"award-info":[{"award-number":["2019YFE01277002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0504102"],"award-info":[{"award-number":["2017YFB0504102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1502704"],"award-info":[{"award-number":["2017YFC1502704"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671412"],"award-info":[{"award-number":["41671412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China\u2019s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.<\/jats:p>","DOI":"10.3390\/rs14010030","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T02:02:57Z","timestamp":1640224977000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Zoning Earthquake Casualty Prediction Model Based on Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Boyi","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Adu","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Tingting","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3510-2002","authenticated-orcid":false,"given":"Wenxuan","family":"Bao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Can","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zhiqing","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.techfore.2017.12.001","article-title":"A scenario-based model for earthquake emergency management effectiveness evaluation","volume":"128","author":"Zhang","year":"2018","journal-title":"Technol. 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