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|Title:||Supervised intelligent committee machine method for hydraulic conductivity estimation||Authors:||Tayfur, Gökmen
Nadiri, Ata A.
Moghaddam, Asghar A.
|Keywords:||Artificial intelligence methods
Supervised intelligence committee machine
|Issue Date:||Mar-2014||Publisher:||Springer Verlag||Source:||Tayfur, G., Nadiri, A. A., and Moghaddam, A. A. (2014). Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resources Management, 28(4), 1173-1184. doi:10.1007/s11269-014-0553-y||Abstract:||Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.||URI:||https://doi.org/10.1007/s11269-014-0553-y
|Appears in Collections:||Civil Engineering / İnşaat Mühendisliği|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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