Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS). Issue 2 (4th May 2019)

• Record Type: Journal Article
• Authors: Sihag, Parveen; Tiwari, N. K.; Ranjan, Subodh
• Is Part Of: ISH journal of hydraulic engineering. Volume 25:Issue 2(2019)
• Page Start: 132
• Page End: 142
• Publication Date: 2019-05-04
• Subjects: unsaturated hydraulic conductivity -- adaptive neuro-fuzzy inference system -- multi-linear regression and artificial neural network; Hydraulic engineering -- Periodicals; Hydraulic engineering -- India -- Periodicals; Hydraulic engineering; India; Periodicals; 627
• DOI: 10.1080/09715010.2017.1381861
• Languages: English
• ISSNs: 0971-5010

Abstract

Abstract: 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.