Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam,Iran |
| |
Authors: | Khalil Rezaei Bernard Guest Anke Friedrich Farajollah Fayazi Muhammad Nakhaei Ali Beitollahi Seyed Mahmoud Fatemi Aghda |
| |
Institution: | (1) Department of Electronic and Computer Education, Technical Education Faculty, Selcuk University, 42005 Konya, Turkey |
| |
Abstract: | An application of the artificial neural network (ANN) approach for predicting mean grain size using electric resistivity data
from Bam city is presented. A feed forward back propagation network was developed employing 45 sets of input data. The input
variables in the ANN model are the electrical resistivity, water table as a Boolean value and depth; the output is the mean
grain size. To demonstrate the authenticity of this approach, the network predictions are compared with those from interpolation
methods and the same data. This comparison shows that the ANN approach performs better results. The predicted and observed
mean grain size values were compared and show high correlation coefficients. The ANN approach maps show a high degree of correlation
with well data based grain size maps and can therefore be used conservatively to better understand the influence of input
parameters on sedimentological predictions. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|