Comparison of three artificial neural network approaches for estimating of slake durability index

TAŞDEMİR Y., KOLAY E., KAYABALI K.

ENVIRONMENTAL EARTH SCIENCES, cilt.68, sa.1, ss.23-31, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 68 Sayı: 1
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s12665-012-1702-3
  • Dergi Adı: ENVIRONMENTAL EARTH SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.23-31
  • Anahtar Kelimeler: Slake durability index, Feed-forward back propagation, Radial basis function based neural network, Generalized regression neural networks, FEEDFORWARD NETWORKS, PREDICTION, ROCK, REGRESSION, EVAPOTRANSPIRATION, SUSCEPTIBILITY, TURKEY, SOILS
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

Slake durability index (I (d2)) is an important engineering parameter to assess the resistance of clay-bearing and weak rocks to erosion and degradation. Standard test sample preparation for slake durability test is difficult for some rock types and the test is time-consuming. The paper reports an attempt to define I (d2) using other parameters that are simpler to obtain. In this study, three different artificial neural network approaches, namely feed-forward back propagation (FFBP), radial basis function based neural network (RBNN), and generalized regression neural networks (GRNN) were used for estimating I (d2). The determination coefficient (R (2)), root mean square error and mean absolute relative error statistics were used as evaluation criteria of the FFBP, RBNN, and GRNN models. The experimental results were compared with these models. The comparison results indicate that the GRNN models are superior to the FFBP and RBNN models in modeling of the slake durability index (I (d2)).