Application of remote sensing and machine learning algorithms for shipwreck susceptibility mapping in China
文献类型：期刊论文
| 作者 | Chen, Junhui1,2,3,4; Tang, Fei2,3,4; Lin, Heshan2,3,4; Chen, Yong2,3,4; Chen, Yuyue2,3,4; Lin, Peiru2,3,4; Huang, Bo2,3,4; Lin, Xueping2,3,4 |
| 刊名 | JOURNAL OF ARCHAEOLOGICAL SCIENCE
 |
| 出版日期 | 2026 |
| 卷号 | 185页码:106429 |
| 关键词 | Shipwreck Remote sensing Machine learning Susceptibility mapping Underwater archaeology |
| ISSN号 | 0305-4403 |
| DOI | 10.1016/j.jas.2025.106429 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Shipwrecks hold dual significance as cultural time capsules and ecological refugia that enhance marine biodiversity. However, systematic, large-scale methods for locating them are still limited. This study presents an innovative approach to map shipwreck susceptibility in Chinese adjacent seas by integrating remote sensing data with machine learning techniques. We assembled a historical shipwreck inventory and analyzed 16 conditioning factors, grouped into geospatial, hydrodynamic, and depositional categories. These factors were processed using Frequency Ratio (FR) values, which served as inputs for three ensemble models: Multi-Layer Perceptron (MLP-FR), Random Forest (RF-FR), and Support Vector Machine (SVM-FR). Model performance was evaluated through statistical metrics and ROC-AUC curves, with the RF-FR model outperforming others, achieving an AUC of 0.995 for training and 0.901 for validation. The resulting susceptibility maps identify priority areas for archaeological exploration. Feature importance analysis revealed proximity to the coastline, chlorophyll concentration, and oceanographic conditions as the primary factors influencing shipwreck occurrence. This scalable, cost-effective framework offers a valuable tool for directing underwater heritage surveys and has potential applications in marine conservation and tourism planning. |
| URL标识 | 查看原文 |
| WOS关键词 | SUPPORT VECTOR MACHINES ; ARTIFICIAL NEURAL-NETWORKS ; FREQUENCY RATIO ; SEA ; FOUGUEUX ; FOREST ; MODEL |
| WOS研究方向 | Anthropology ; Archaeology ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001622759100001 |
| 出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219767]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Junhui |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.MNR, Observat & Res Stn Isl & Coastal Ecosyst Western, Fuzhou, Peoples R China; 3.MNR, Isl Res Ctr, Fujian Key Lab Isl Monitoring & Ecol Dev, Fuzhou, Fujian, Peoples R China; 4.MNR, Isl Res Ctr, Fuzhou, Fujian, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen, Junhui,Tang, Fei,Lin, Heshan,et al. Application of remote sensing and machine learning algorithms for shipwreck susceptibility mapping in China[J]. JOURNAL OF ARCHAEOLOGICAL SCIENCE,2026,185:106429. |
| APA | Chen, Junhui.,Tang, Fei.,Lin, Heshan.,Chen, Yong.,Chen, Yuyue.,...&Lin, Xueping.(2026).Application of remote sensing and machine learning algorithms for shipwreck susceptibility mapping in China.JOURNAL OF ARCHAEOLOGICAL SCIENCE,185,106429. |
| MLA | Chen, Junhui,et al."Application of remote sensing and machine learning algorithms for shipwreck susceptibility mapping in China".JOURNAL OF ARCHAEOLOGICAL SCIENCE 185(2026):106429. |
入库方式： OAI收割
来源：地理科学与资源研究所
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