New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks
This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear P...
Gespeichert in:
| Veröffentlicht in: | Scientific reports 2021-01, Vol.11 (1), p.1857-11, Article 1857 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11 |
|---|---|
| container_issue | 1 |
| container_start_page | 1857 |
| container_title | Scientific reports |
| container_volume | 11 |
| creator | Sasaki, Miyuki Sanada, Yukihisa Katengeza, Estiner W. Yamamoto, Akio |
| description | This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. Much of the survey data obtained in the past were used as the training data for building a network. The number of training cases was related to the error between the ground and converted values by the ANN. The quantitative evaluation index (the root-mean-square error) between the ANN-converted value and the ground-based survey result converged at 200 training cases. This number of training case was considered a rough criterion of the required number of training cases. The reliability of the ANN method was evaluated by comparison with the ground-based survey data. The dose rate map created by the ANNs method reproduced ground-based survey results better than traditional methods. |
| doi_str_mv | 10.1038/s41598-021-81546-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_proquest_journals_2479200000</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_c30641db1c514e169da874cb86efc8e9</doaj_id><sourcerecordid>2479200000</sourcerecordid><originalsourceid>FETCH-LOGICAL-c606t-338d5be39bf13baca7246b9bf71808c8c54c8c5962f07d62a7715663b6a15aa73</originalsourceid><addsrcrecordid>eNp9Ustu1TAQjRCIVqU_wAJZYh3wK7azQUKFQqWqsIC1NXGcG9_mxsWPXsEP8Ns4N6W0G7yYGdtnzpzRTFW9JPgNwUy9jZw0raoxJbUiDRc1f1IdU8ybmjJKnz6Ij6rTGLe4nIa2nLTPqyPGuGRE4OPq95Xdo51No-_R4AO6dTHD5H65eYPSaFHvo0UBUolcTMF1OTk_Iwg-z_0BcZ6vcxzdDtAHcM6MDl1lM1kI6Kvf22InmBPKcWGEkNzgjIMJzTaHg0t7H67ji-rZAFO0p3f-pPp-_vHb2ef68suni7P3l7URWKSaMdU3nWVtNxDWgQFJuejKTRKFlVGm4YtpBR2w7AUFKUkjBOsEkAZAspPqYuXtPWz1TSi6w0_twenDgw8bvYgsDWjDsOCk74hpCLdEtD0oyU2nhB2Msm3herdy3eRuZ3tj51RaekT6-Gd2o974Wy0VkUIsYl7fEQT_I9uY9NbnMJf-NeWypcvMcEHRFWWCjzHY4b4CwXrZBb3ugi67oA-7oHlJevVQ233K38kXAFsBsXzNGxv-1f4P7R9SnsJE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2479200000</pqid></control><display><type>article</type><title>New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Springer Nature OA Free Journals</source><source>Nature Free</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><source>Free Full-Text Journals in Chemistry</source><creator>Sasaki, Miyuki ; Sanada, Yukihisa ; Katengeza, Estiner W. ; Yamamoto, Akio</creator><creatorcontrib>Sasaki, Miyuki ; Sanada, Yukihisa ; Katengeza, Estiner W. ; Yamamoto, Akio</creatorcontrib><description>This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. Much of the survey data obtained in the past were used as the training data for building a network. The number of training cases was related to the error between the ground and converted values by the ANN. The quantitative evaluation index (the root-mean-square error) between the ANN-converted value and the ground-based survey result converged at 200 training cases. This number of training case was considered a rough criterion of the required number of training cases. The reliability of the ANN method was evaluated by comparison with the ground-based survey data. The dose rate map created by the ANNs method reproduced ground-based survey results better than traditional methods.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-021-81546-4</identifier><identifier>PMID: 33473160</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>639/705 ; 704/172/169/895 ; Humanities and Social Sciences ; multidisciplinary ; Neural networks ; Nuclear power plants ; Polls & surveys ; Science ; Science (multidisciplinary)</subject><ispartof>Scientific reports, 2021-01, Vol.11 (1), p.1857-11, Article 1857</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c606t-338d5be39bf13baca7246b9bf71808c8c54c8c5962f07d62a7715663b6a15aa73</citedby><cites>FETCH-LOGICAL-c606t-338d5be39bf13baca7246b9bf71808c8c54c8c5962f07d62a7715663b6a15aa73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817667/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817667/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,27905,27906,41101,42170,51557,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33473160$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sasaki, Miyuki</creatorcontrib><creatorcontrib>Sanada, Yukihisa</creatorcontrib><creatorcontrib>Katengeza, Estiner W.</creatorcontrib><creatorcontrib>Yamamoto, Akio</creatorcontrib><title>New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. Much of the survey data obtained in the past were used as the training data for building a network. The number of training cases was related to the error between the ground and converted values by the ANN. The quantitative evaluation index (the root-mean-square error) between the ANN-converted value and the ground-based survey result converged at 200 training cases. This number of training case was considered a rough criterion of the required number of training cases. The reliability of the ANN method was evaluated by comparison with the ground-based survey data. The dose rate map created by the ANNs method reproduced ground-based survey results better than traditional methods.</description><subject>639/705</subject><subject>704/172/169/895</subject><subject>Humanities and Social Sciences</subject><subject>multidisciplinary</subject><subject>Neural networks</subject><subject>Nuclear power plants</subject><subject>Polls & surveys</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNp9Ustu1TAQjRCIVqU_wAJZYh3wK7azQUKFQqWqsIC1NXGcG9_mxsWPXsEP8Ns4N6W0G7yYGdtnzpzRTFW9JPgNwUy9jZw0raoxJbUiDRc1f1IdU8ybmjJKnz6Ij6rTGLe4nIa2nLTPqyPGuGRE4OPq95Xdo51No-_R4AO6dTHD5H65eYPSaFHvo0UBUolcTMF1OTk_Iwg-z_0BcZ6vcxzdDtAHcM6MDl1lM1kI6Kvf22InmBPKcWGEkNzgjIMJzTaHg0t7H67ji-rZAFO0p3f-pPp-_vHb2ef68suni7P3l7URWKSaMdU3nWVtNxDWgQFJuejKTRKFlVGm4YtpBR2w7AUFKUkjBOsEkAZAspPqYuXtPWz1TSi6w0_twenDgw8bvYgsDWjDsOCk74hpCLdEtD0oyU2nhB2Msm3herdy3eRuZ3tj51RaekT6-Gd2o974Wy0VkUIsYl7fEQT_I9uY9NbnMJf-NeWypcvMcEHRFWWCjzHY4b4CwXrZBb3ugi67oA-7oHlJevVQ233K38kXAFsBsXzNGxv-1f4P7R9SnsJE</recordid><startdate>20210120</startdate><enddate>20210120</enddate><creator>Sasaki, Miyuki</creator><creator>Sanada, Yukihisa</creator><creator>Katengeza, Estiner W.</creator><creator>Yamamoto, Akio</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20210120</creationdate><title>New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks</title><author>Sasaki, Miyuki ; Sanada, Yukihisa ; Katengeza, Estiner W. ; Yamamoto, Akio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c606t-338d5be39bf13baca7246b9bf71808c8c54c8c5962f07d62a7715663b6a15aa73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>639/705</topic><topic>704/172/169/895</topic><topic>Humanities and Social Sciences</topic><topic>multidisciplinary</topic><topic>Neural networks</topic><topic>Nuclear power plants</topic><topic>Polls & surveys</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sasaki, Miyuki</creatorcontrib><creatorcontrib>Sanada, Yukihisa</creatorcontrib><creatorcontrib>Katengeza, Estiner W.</creatorcontrib><creatorcontrib>Yamamoto, Akio</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sasaki, Miyuki</au><au>Sanada, Yukihisa</au><au>Katengeza, Estiner W.</au><au>Yamamoto, Akio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2021-01-20</date><risdate>2021</risdate><volume>11</volume><issue>1</issue><spage>1857</spage><epage>11</epage><pages>1857-11</pages><artnum>1857</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. Much of the survey data obtained in the past were used as the training data for building a network. The number of training cases was related to the error between the ground and converted values by the ANN. The quantitative evaluation index (the root-mean-square error) between the ANN-converted value and the ground-based survey result converged at 200 training cases. This number of training case was considered a rough criterion of the required number of training cases. The reliability of the ANN method was evaluated by comparison with the ground-based survey data. The dose rate map created by the ANNs method reproduced ground-based survey results better than traditional methods.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33473160</pmid><doi>10.1038/s41598-021-81546-4</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2045-2322 |
| ispartof | Scientific reports, 2021-01, Vol.11 (1), p.1857-11, Article 1857 |
| issn | 2045-2322 2045-2322 |
| language | eng |
| recordid | cdi_proquest_journals_2479200000 |
| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Springer Nature OA Free Journals; Nature Free; PubMed Central; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry |
| subjects | 639/705 704/172/169/895 Humanities and Social Sciences multidisciplinary Neural networks Nuclear power plants Polls & surveys Science Science (multidisciplinary) |
| title | New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T18%3A52%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=New%20method%20for%20visualizing%20the%20dose%20rate%20distribution%20around%20the%20Fukushima%20Daiichi%20Nuclear%20Power%20Plant%20using%20artificial%20neural%20networks&rft.jtitle=Scientific%20reports&rft.au=Sasaki,%20Miyuki&rft.date=2021-01-20&rft.volume=11&rft.issue=1&rft.spage=1857&rft.epage=11&rft.pages=1857-11&rft.artnum=1857&rft.issn=2045-2322&rft.eissn=2045-2322&rft_id=info:doi/10.1038/s41598-021-81546-4&rft_dat=%3Cproquest_doaj_%3E2479200000%3C/proquest_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2479200000&rft_id=info:pmid/33473160&rft_doaj_id=oai_doaj_org_article_c30641db1c514e169da874cb86efc8e9&rfr_iscdi=true |