{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T23:41:32Z","timestamp":1660520492951},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"To meet the increasingly urgent need for ecological restoration effect evaluation, the ecosystem health status of reefs was described, and an ecosystem health evaluation model of artificial reefs (ARs) based on Bayesian networks (BNs) was established. By comparing the probability of the ecological health status between restored areas and control areas, we assessed the AR ecological restoration effect of the Qinhuangdao project in 2012. The results show that this project had a remediating effect in May and September, with clear repair effects on the water environment, sediment environment, and fishery resources. The sensitivity of each indicator was calculated, and organic carbon and benthos biomass was identified as the most sensitive factors. This study will provide the basis for the further development of restoration measures.<\/jats:p>","DOI":"10.3233\/faia220123","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T23:22:07Z","timestamp":1660519327000},"source":"Crossref","is-referenced-by-count":0,"title":["Restoration Effect Evaluation of Artificial Reefs Based on Bayesian Networks"],"prefix":"10.3233","author":[{"given":"Xin","family":"Lv","sequence":"first","affiliation":[{"name":"Second Institute of Oceanography, Ministry of Nature Resources, Hangzhou 310012, China"}]},{"given":"Yifei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Second Institute of Oceanography, Ministry of Nature Resources, Hangzhou 310012, China"}]},{"given":"Xin","family":"Fang","sequence":"additional","affiliation":[{"name":"Second Institute of Oceanography, Ministry of Nature Resources, Hangzhou 310012, China"},{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210093, China"}]},{"given":"Zonghao","family":"Hou","sequence":"additional","affiliation":[{"name":"Second Institute of Oceanography, Ministry of Nature Resources, Hangzhou 310012, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Modern Management based on Big Data III"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220123","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T23:22:09Z","timestamp":1660519329000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,10]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220123","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,10]]}}}