...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural computing & applications >A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution
【24h】

A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution

机译：土壤中总芳烃预测的支持向量回归模型：一种绘制环境污染的人工智能系统

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The significance of total polyaromatic hydrocarbons (TPAH) determination in assessing the carcinogenicity of environmental samples for measuring the level of environmental pollution cannot be overemphasized. Despite the environmental danger of TPAH, its laboratory quantification is laborious, which consumes appreciable time and other valuable resources. This research work develops a computational intelligence-based model for the first time, which directly estimates and quantifies the level of TPAH of any environmental solid samples using total petroleum hydrocarbons descriptor that can be easily determined experimentally. The hyperparameters of the developed support vector regression (SVR)-based model are optimized using manual search (MS) approach and genetic algorithm (GA) search approach with Gaussian and polynomial kernel functions. Experimental validation of the developed model was carried out using samples obtained from the marine sediments of Arabian Gulf Sea. The future generalization and predictive strength of the developed models were assessed using correlation coefficient (CC), root-mean-square error, mean absolute error and mean absolute percentage deviation (MAPD). GA-SVR-Gaussian performs better than MS-SVR and GA-SVR-poly with performance enhancement of 63.89% and 536.32%, respectively, on the basis of MAPD as a performance-measuring parameter, while MS-SVR model performs better than GA-SVR-poly with performance improvement of 288.25% using MAPD to evaluate the model performance. The estimation accuracy and generalization strength of the developed models indicate the potential of the models in measuring the level of environmental pollution of oil-spilled area without experimental stress, while experimental precision is preserved.
机译：总芳烃（TPAH）测定在评估环境样品致癌水平中测量环境污染水平的意义，不能赘述。尽管TPAH的环境危险，但其实验室量化是费力的，这消耗了可观的时间和其他有价值的资源。该研究工作首次开发了基于计算智能的模型，其使用总石油烃描述符直接估计和量化任何环境固体样品的TPAH水平，这可以通过实验易于确定。基于开发的支持向量回归（SVR）的型号的超参数使用手动搜索（MS）方法和遗传算法（GA）搜索方法与高斯和多项式内核功能进行了优化。使用从阿拉伯海湾海洋海洋沉积物获得的样品进行开发模型的实验验证。使用相关系数（CC），根均方误差，平均绝对误差和平均绝对百分比偏差（MAPD）评估开发模型的未来泛化和预测强度。 GA-SVR-Gaussian在MAPD作为性能测量参数的基础上，PA-SVR-Gaussian分别表现优于MS-SVR和GA-SVR-Poly，分别为63.89％和536.32％，而MS-SVR模型比GA更好地执行-SVR-Poly使用MAPD的性能提高288.25％，以评估模型性能。开发模型的估计准确度和泛化强度表示模型测量在没有实验应力的情况下测量溢油区域的环境污染水平，而实验精度保存。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号