{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:11:07Z","timestamp":1791648667972,"version":"4.3.4"},"reference-count":75,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSPS Grants-in-Aid for Scientific Research","award":["18K18536"],"award-info":[{"award-number":["18K18536"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Archaeological predictive modeling (APM) is an essential method for quantitatively assessing the probability of archaeological sites present in a region. It is a necessary tool for archaeological research and cultural heritage management. In particular, the predictive modeling process could help us understand the relationship between past human civilizations and the natural environment; moreover, a better understanding of the mechanisms of the human\u2013land relationship can provide new ideas for sustainable development. This study aims to investigate the impact of topographic and hydrological factors on archaeological sites in the Japanese archipelago and Shaanxi Province, China and proposes a hybrid integration approach for APM. This approach employed a conditional attention mechanism (AM) using deep learning and a frequency ratio (FR) model, in addition to a separate FR model and the widely-used machine learning MaxEnt method. The models\u2019 outcomes were cross-checked using the four-fold cross-validation method, and the models\u2019 performances were compared using the area under the receiver operating characteristic curve (AUC) and Kvamme\u2019s Gain. The results showed that in both study areas, the AM_FR model exhibited the most satisfactory performances.<\/jats:p>","DOI":"10.3390\/ijgi12060238","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T01:34:32Z","timestamp":1686188072000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China"],"prefix":"10.3390","volume":"12","author":[{"given":"Yuan","family":"Wang","sequence":"first","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8563, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Shi","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8563, Japan"},{"name":"School of Business, Society and Technology, M\u00e4lardalens University, 72123 V\u00e4ster\u00e5s, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8340-6994","authenticated-orcid":false,"given":"Takashi","family":"Oguchi","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8563, Japan"},{"name":"Center for Spatial Information Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8568, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/B978-0-12-003109-2.50011-8","article-title":"Predictive Models for Archaeological Resource Location","volume":"9","author":"Kohler","year":"1986","journal-title":"Adv. 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