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Water Resources Management

Erschienen in:

01.03.2014

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

verfasst von: Gokmen Tayfur, Ata A. Nadiri, Asghar A. Moghaddam

Erschienen in: Water Resources Management | Ausgabe 4/2014

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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.

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Titel: Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation
verfasst von: Gokmen Tayfur
Ata A. Nadiri
Asghar A. Moghaddam
Publikationsdatum: 01.03.2014
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 4/2014
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-014-0553-y

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