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Developing cation exchange capacity and soil index properties relationships using a neuro-fuzzy approach

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Date
2014-10
Author
Pulat, Hasan Fırat
Tayfur, Gökmen
Yükselen Aksoy, Yeliz
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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
Collections
  • Civil Engineering / İnşaat Mühendisliği [231]
  • Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection [3276]
  • WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection [2953]


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