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Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Sihag, Parveen / Tiwari, N. K. / Ranjan, Subodh

This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro- fuzzy inference system (ANFIS), Multi-Linear Regression (MLR), and artificial neural network (ANN). Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data-set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell-shaped membership-based ANFIS. MLR is better than ANN and Gaussian membership function-based ANFIS for unsaturated hydraulic conductivity.

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Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Sihag, Parveen / Tiwari, N. K. / Ranjan, Subodh

This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro- fuzzy inference system (ANFIS), Multi-Linear Regression (MLR), and artificial neural network (ANN). Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data-set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell-shaped membership-based ANFIS. MLR is better than ANN and Gaussian membership function-based ANFIS for unsaturated hydraulic conductivity.

Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Sihag, Parveen / Tiwari, N. K. / Ranjan, Subodh

This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro- fuzzy inference system (ANFIS), Multi-Linear Regression (MLR), and artificial neural network (ANN). Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data-set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell-shaped membership-based ANFIS. MLR is better than ANN and Gaussian membership function-based ANFIS for unsaturated hydraulic conductivity.

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Title:

Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Contributors:

Sihag, Parveen (author) / Tiwari, N. K. (author) / Ranjan, Subodh (author)

Published in:

ISH Journal of Hydraulic Engineering ; 25 ; 132-142

Publication date:

2019-05-04

Size:

11 pages

ISSN:

0971-5010 , 2164-3040

DOI:

https://doi.org/10.1080/09715010.2017.1381861

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

unsaturated hydraulic conductivity , adaptive neuro-fuzzy inference system , multi-linear regression and artificial neural network

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