Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5577
Title: Developing cation exchange capacity and soil index properties relationships using a neuro-fuzzy approach
Authors: Pulat, Hasan Fırat
Tayfur, Gökmen
Yükselen Aksoy, Yeliz
Keywords: Artificial intelligence method
Artificial neural network
Cation exchange capacity
Clayey soils
Fuzzy logic
Soil index properties
Issue Date: Oct-2014
Publisher: Springer Verlag
Source: Pulat, H.F., Tayfur, G., and Yükselen Aksoy, Y. (2014). Developing cation exchange capacity and soil index properties relationships using a neuro-fuzzy approach. Bulletin of Engineering Geology and the Environment, 73(4), 1141-1149. doi:10.1007/s10064-014-0644-2
Abstract: Artificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R2 = 0.94 and mean absolute error, (MAE) = 7.1.
URI: https://doi.org/10.1007/s10064-014-0644-2
http://hdl.handle.net/11147/5577
ISSN: 1435-9529
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
5577.pdfMakale534.41 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

2
checked on Dec 11, 2021

WEB OF SCIENCETM
Citations

3
checked on Jan 22, 2022

Page view(s)

66
checked on Jan 24, 2022

Download(s)

60
checked on Jan 24, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.