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Correction Model-Based ANN Modeling Approach for the Estimation of Radon Concentrations in Ohio

机译：基于校正模型的人工神经网络建模方法估算俄亥俄州的Ohio气浓度

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摘要

According to National Cancer Institute, radon is one of the major causes for lung cancer related deaths after smoking in US. To prevent deaths due to radon inhalation there is a need to determine the level of radon concentration in each locality, for example, zip code and this would help ease the identification of areas with high radon concentration thereby allowing the necessary preventive measures to be taken. However, factors like inapproachability hinder the process of estimating radon concentration in some places. In such places it is a common practice to estimate the radon concentrations using several interpolation techniques. In this article, a new approach that improves the accuracy of the neural model with the help of sensitivity-based correction model for modeling and estimating radon concentrations in Ohio is proposed. The results are compared with commonly used techniques such as kriging, radial basis function (RBF), inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), and the recently developed conventional ANN modeling approach. Further, model accuracies of all the above models are evaluated based on Willmott's Index and the ranked performance measures criteria with emphasis on the extreme-end (peak-end, low-end), and mid-range radon concentrations. The results demonstrate the effectiveness of the proposed approach in estimating the radon concentrations with the percentage improvement of 70-80% prediction accuracy, compared to the other techniques.
机译：根据美国国家癌症研究所的数据，ra是美国吸烟引起的肺癌相关死亡的主要原因之一。为了防止由于吸入ra而导致的死亡，需要确定每个地区的concentration浓度水平，例如邮政编码，这将有助于简化对with浓度高的区域的识别，从而可以采取必要的预防措施。但是，诸如接近性之类的因素阻碍了某些地方ra浓度的估算过程。在这样的地方，通常的做法是使用几种插值技术来估算the浓度。在本文中，提出了一种新的方法，该方法借助于基于灵敏度的校正模型来对俄亥俄州的concentrations气浓度进行建模和估计，从而可以提高神经模型的准确性。将结果与常用技术进行比较，例如克里金法，径向基函数（RBF），反距离权重（IDW），全局多项式插值（GPI），局部多项式插值（LPI）和最近开发的常规ANN建模方法。此外，上述所有模型的模型准确性都是基于Willmott指数和排名的性能度量标准进行评估的，重点是极端（峰值，低端）和中范围的don浓度。结果表明，与其他技术相比，该方法在估计the浓度方面的有效性提高了70-80％的预测准确度。

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  • 来源
    《Environmental progress》 |2013年第4期|1223-1233|共11页
  • 作者

    Pavan Yerrabolu; Lakshman Mareddy; Deepak Bhatt; Priyanka Aggarwal; Ashok Kumar; Vijay Devabhaktuni;

  • 作者单位

    EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH, 43606;

    EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH, 43606;

    EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH, 43606;

    EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH, 43606;

    Department of Civil Engineering, University of Toledo, MS 307, 2801 W. Bancroft St., Toledo, OH, 43606;

    EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH, 43606;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural networks; correction model; indoor air quality measures; interpolation; Ohio; radon;

    机译：人工神经网络;校正模型;室内空气质量措施;插值俄亥俄州;氡;

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