A novel hybrid approach to the mapping and prediction of the terrestrial gamma dose rate distribution in the Central Anatolia Region of Turkey
| dc.authorid | 0000-0002-7508-7548 | en_US |
| dc.contributor.author | Yeşilkanat, Cafer Mert | |
| dc.date.accessioned | 2019-10-23T13:29:04Z | |
| dc.date.available | 2019-10-23T13:29:04Z | |
| dc.date.issued | 2019 | |
| dc.department | AÇÜ, Eğitim Fakültesi | en_US |
| dc.description.abstract | In this study, a new approach was suggested for the estimation and mapping of Terrestrial gamma dose rate (TGDR, in nGy h?1 ) in the Central Anatolia Region of Turkey by using the sequential Gaussian simulation (SGS) and Artificial neural network (ANN) methods together as a hybrid. In this hybrid approach (SGS-ANN), different from the classical spatial examinations, each spatial pixel (500 500 × m2 ) were calculated separately by evaluating the activity concentrations of terrestrial radionuclides that directly affects TGDR (226Ra, 232Th and 40K, in Bq kg?1 ) terrestrial coordination (X and Y, in meter). Therefore, the local changes of TGDR distributions that were estimated for the study area could be determined in appropriate precision without the smoothing effect. The performance evaluation of SGS-ANN approach was conducted by comparing the results for the same study area of Ordinary kriging (OK) method which is frequently used in the literature. According to the validation diagram that was created with the observed and estimated TGDR values, the Pearson's r correlation coefficient was obtained as 0.30 and 0.65, RMSE as 31.41 nGy h?1 and 25.79 nGy h?1 , MAE as 24.50 nGy h?1 and 21.29 nGy h?1 and mean error as 5.97 nGy h?1 and -1.32 nGy h?1 for the OK method and the SGS-ANN approach, respectively | |
| dc.identifier.citation | Yeşilkanat, C. M. (2019). A novel hybrid approach to the mapping and prediction of the terrestrial gamma dose rate distribution in the Central Anatolia Region of Turkey. Journal of environmental radioactivity, 208, 106009. | en_US |
| dc.identifier.doi | 10.1016/j.jenvrad.2019.106009 | |
| dc.identifier.endpage | 13 | en_US |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | https://hdl.handle.net/11494/1815 | |
| dc.identifier.volume | 208 | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.institutionauthor | Yeşilkanat, Cafer Mert | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | Journal of Environmental Radioactivity | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
| dc.subject | Terrestrial gamma dose rate | en_US |
| dc.subject | Sequential Gaussian simulation | en_US |
| dc.subject | Artificial neural network | en_US |
| dc.subject | Central Anatolia region of Turkey | en_US |
| dc.subject | Radionuclides | en_US |
| dc.title | A novel hybrid approach to the mapping and prediction of the terrestrial gamma dose rate distribution in the Central Anatolia Region of Turkey | en_US |
| dc.type | Article |
