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首页> 外文期刊>Applied radiation and isotopes: including data, instrumentation and methods for use in agriculture, industry and medicine >Estimation of radon flux spatial distribution in Rize, Turkey by the artificial neural networks method
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Estimation of radon flux spatial distribution in Rize, Turkey by the artificial neural networks method

机译：人工神经网络法雷氡磁通空间分布估算雷电空间分布

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摘要

In this study, average radon flux distribution in the Rize province (Turkey) was estimated by the artificial neural networks (ANN) method. For this purpose, terrestrial gamma dose rate (TGDR), which is defined as an important proxy in determining radon flux distribution, was used. Input parameters that were used for ANN were the natural radionuclide (U-238, Th-232 and K-40) activity values in soil samples taken from 64 stations in Rize Province, data from ambient gamma dose rates (AGDR) directly affecting the distribution of radon flux and data of geographical coordinates. Randomly chosen 42 stations were used for ANN training and data from 22 stations were used for testing the ANN model. Performance test results gave a Pearson's r value of 0.60 (p < 0.001) and RMSE of 0.296. The area that was used for the model was divided into grids of 100 m by 100 m and a spatial distribution map was composed by using ANN predicted radon flux rates at grid nodes, whereby natural radionuclide values and Ordinary Kriging predicted values of external gamma dose rates were used for composing the map.
机译：在本研究中，通过人工神经网络（ANN）方法估计了RIZE省（土耳其）中的平均氡通量分布。为此目的，使用了陆地γ剂量率（TGDR），其被定义为在确定氡通量分布中的重要代理。用于ANN的输入参数是从64位在RIZE省的64个站中获取的土壤样品中的天然放射性核素（U-238，TH-232和K-40）活性值，来自环境伽玛剂量率（AGDR）的数据直接影响分布氡通量和地理坐标数据的影响。随机选择的42个站用于ANN培训，并且来自22个站的数据用于测试ANN模型。性能测试结果使Pearson的R值为0.60（P <0.001）和0.296的RMSE。用于该模型的该区域被分成100μm的网格100μm，并且通过在网格节点处使用ANN预测的氡通量速率来组成空间分布图，其中天然放射性核素值和外部γ剂量率的普通克里格预测值用于组成地图。

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