{"created":"2023-06-19T12:48:41.868613+00:00","id":9434,"links":{},"metadata":{"_buckets":{"deposit":"bf8f50ab-b618-43ec-9bd4-efa75daa954d"},"_deposit":{"created_by":14,"id":"9434","owners":[14],"pid":{"revision_id":0,"type":"depid","value":"9434"},"status":"published"},"_oai":{"id":"oai:tokyo-metro-u.repo.nii.ac.jp:00009434","sets":["521:522:524:525:2056"]},"author_link":["30597","30595","30596"],"item_3_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"11","bibliographicPageStart":"1","bibliographicVolumeNumber":"57","bibliographic_titles":[{},{"bibliographic_title":"Geographical reports of Tokyo Metropolitan University","bibliographic_titleLang":"en"}]}]},"item_3_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Using machine learning (ML) algorithms to digital soil mapping (DSM) allows the\nelucidation of relationships between soil properties and environmental variables enabling the precise prediction of soil nutrient levels. The accuracy of the predicted values using the random forest (RF) algorithm, which is the most popular ML algorithm for digital soil mapping, and multiple linear regression (MLR) were compared to create digital maps of soil chemical properties in the Thung\nKula Ronghai (TKR) region, Thailand. The spectral indices including moisture stress index (MSI), normalized difference water index (NDWI), saturation index (SI), brightness index (BI), and coloration index (CI) obtained from remote sensing (RS) data were found to be more effective for predicting the various soil properties than the topographic indices derived from the DEM in the plain area. The MLR and RF models successfully predicted soil chemical properties with good predictive\naccuracy. The results indicated that the RF model has a slightly higher accuracy than the MLR model.\nHowever, the MLR model is superior in interpreting the relationship with the model equations.","subitem_description_type":"Abstract"}]},"item_3_publisher_33":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Department of Geography Tokyo Metropolitan University"}]},"item_3_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA00200173","subitem_source_identifier_type":"NCID"}]},"item_3_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"03868710","subitem_source_identifier_type":"ISSN"}]},"item_3_version_type_16":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Srisomkiew, Sasirin"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kawahigashi, Masayuki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Limtong, Pitayakon"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-03-29"}],"displaytype":"detail","filename":"20005-057-001.pdf","filesize":[{"value":"2.0 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"20005-057-001.pdf","url":"https://tokyo-metro-u.repo.nii.ac.jp/record/9434/files/20005-057-001.pdf"},"version_id":"c41c95e1-4b83-463d-98d7-eb5a138923ac"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"environmental variables","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"predictor variable","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"spatial distribution","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"spectral indices","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"topographic indices","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Comparison between random forest and multiple linear regression to create digital maps of soil chemical properties in the Thung Kula Ronghai region, Thailand","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Comparison between random forest and multiple linear regression to create digital maps of soil chemical properties in the Thung Kula Ronghai region, Thailand","subitem_title_language":"en"}]},"item_type_id":"3","owner":"14","path":["2056"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-29"},"publish_date":"2022-03-29","publish_status":"0","recid":"9434","relation_version_is_last":true,"title":["Comparison between random forest and multiple linear regression to create digital maps of soil chemical properties in the Thung Kula Ronghai region, Thailand"],"weko_creator_id":"14","weko_shared_id":-1},"updated":"2023-06-19T14:36:40.380630+00:00"}