• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Hydroinformatics >Supervised committee machine with artificial intelligence for prediction of fluoride concentration
【24h】

Supervised committee machine with artificial intelligence for prediction of fluoride concentration

机译：带有人工智能的监督委员会机器，用于预测氟化物浓度

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 study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of Al model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.
机译：该研究引入了一种带有人工智能（SCMAI）方法的监督委员会机器来预测伊朗马库地下水中的氟化物。地下水是该地区饮用水的主要来源。处理氟化物异常需要更好地预测氟化物浓度。然而，复杂的水文地质特征导致难以准确预测玄武岩地层，非玄武岩地层和混合带中的氟化物浓度。 SCMAI通过人工智能系统通过单个AI模型的非线性组合来预测氟化物。因子分析用于识别与氟化物相关的有效水化学参数，作为AI模型的输入。四个AI模型，Sugeno模糊逻辑，Mamdani模糊逻辑，人工神经网络（ANN）和神经模糊可用于预测氟化物浓度。结果表明，所有这些模型都与马库地区的氟化物数据具有相似的拟合度，并且对于混合区中的样品预测不佳。 SCMAI使用ANN模型，根据四个AI模型预测来重新预测氟化物浓度。结果显示对CMAI方法的改进，该方法是Al模型预测的线性组合的委员会机器。结果还显示出对单个AI模型的显着拟合改进，尤其是对于混合区中的氟化物预测。

著录项

  • 来源
    《Journal of Hydroinformatics》 |2013年第4期|1474-1490|共17页
  • 作者

    Ata Allah Nadiri; Elham Fijani; Frank T.-C. Tsai; Asghar Asghari Moghaddam;

  • 作者单位

    Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA,Department of Geology, Faculty of Science, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran;

    Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA,Department of Geology, Faculty of Science, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran;

    Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA;

    Department of Geology, Faculty of Science, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial intelligence; artificial neural network; committee machine; fuzzy logic; ground water quality; neuro-fuzzy;

    机译：人工智能;人工神经网络;委员会机;模糊逻辑;地下水水质;神经模糊;

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