Estimation of soil dispersivity using soft computing approaches
作者: Samad EmamgholizadehKiana BahmanS. Mohyeddin BateniHadi GhorbaniIsa MarofpoorJeffrey R. Nielson
作者单位: 1Shahrood University of Technology
2University of Hawaii at Manoa
3University of Kurdistan
刊名: Neural Computing and Applications, 2017, Vol.28 (1), pp.207-216
来源数据库: Springer Nature Journal
DOI: 10.1007/s00521-016-2320-x
关键词: Soil dispersivityAdaptive neuro-fuzzy inference systemArtificial neural networkGenetic expression programmingMultiple linear regression
原始语种摘要: The accurate estimation of soil dispersivity ( α ) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant ( L ), mean grain size ( D 50), soil bulk density ( ρ b), and contaminant velocity ( V c). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination ( R 2)], it is found that all approaches (ANN,...
全文获取路径: Springer Nature  (合作)
影响因子:1.168 (2012)

  • dispersivity 分散性
  • computing 计算
  • accurate 精确的
  • fuzzy 模糊的
  • estimate 估计
  • travel 旅行
  • consuming 耗的
  • ANN All Numeral Numbering
  • outperform 优越
  • density 密度