{"created":"2023-05-15T14:48:24.373050+00:00","id":66328,"links":{},"metadata":{"_buckets":{"deposit":"a4aa56cc-6931-47df-9a41-6aa476b07379"},"_deposit":{"created_by":1,"id":"66328","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"66328"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00066328","sets":["10:29"]},"author_link":["652617","652620","652618","652616","652619"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2017-05-16","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Identifying factors which control the temporal dynamics of physical quantities is a recurrent problem in physics. One example is time series of radon concentration in the air, which is influenced by environmental variable such as temperature, humidity, atmospheric stability, and so on. \nUnderstanding the behavior of radon variability in the environment over long-period is necessary in terms of the indoor radon concentration, which is regulated in e.g. the European Union and the US. \nThis presentation provides an example of the application of five machine learning methods to the indoor radon concentration data obtained at authors’ institute for the period between 2011 and 2016. \nThese methods were employed to reveal the factors influencing the radon dynamics among various meteorological quantities. The performance of each method was evaluated using six statistical metrics, namely the root mean square error (RMSE), the mean absolute error (MAE), the index of agreement (IA), the fractional bias (FB), ratio (RI) and adjusted coefficient of determination (R2adj).\nAs a result, it turned out that the Random Forest method was superior to the other methods. More than 80% of the indoor radon concentration values could be explained by this method as a function of temperature, relatively humidity and day of the year. \nIn comparison, only 35% of the values were explained by a conventional multiple regression analysis using eight predictor quantities. ","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"The Third East-European Radon Symposium (TEERAS 2017) における発表","subitem_description_type":"Other"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"ミロソラフ, ヤニック"}],"nameIdentifiers":[{"nameIdentifier":"652616","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Bossew, Peter"}],"nameIdentifiers":[{"nameIdentifier":"652617","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kurihara, Osamu"}],"nameIdentifiers":[{"nameIdentifier":"652618","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"ミロソラフ ヤニック","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"652619","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"栗原 治","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"652620","nameIdentifierScheme":"WEKO"}]}]},"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":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-05-22"},"publish_date":"2017-05-22","publish_status":"0","recid":"66328","relation_version_is_last":true,"title":["Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T20:50:11.129738+00:00"}