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Symmetry 2019, 11(1), 16; https://doi.org/10.3390/sym11010016

Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China

1
College of Construction Engineering, Jilin University, Changchun 130026, China
2
Department of Civil Engineering, Shanghai University, Shanghai 200444, China
3
College of Surveying and Exploration Engineering, Jilin Jianzhu University, Changchun 130026, China
*
Author to whom correspondence should be addressed.
Received: 18 October 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 25 December 2018
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Abstract

Saline soil in seasonally frozen soil areas has caused tremendous damage to engineering and the ecological environment. The unfrozen water is the main factor affecting the properties of saline soil in seasonally frozen soil area and therefore needs to be studied. However, due to the high cost of laboratory measurement of the unfrozen water content, this study focuses on using an adaptive network fuzzy inference system (ANFIS) and a back propagation neural network (BPNN) to predict the unfrozen water content of saline soil in the Zhenlai area, Western Jilin. The data for the constructed model is obtained by nuclear magnetic resonance (NMR) testing. The initial water content (W0), salt content (S), and temperature (T) are used as input parameters for predicting the unfrozen water content (Wu). The results of the ANFIS and BPNN models are compared. The results show that although both methods are suitable for predicting the unfrozen water content of saline soil in the Zhenlai area, western Jilin, the prediction accuracy of the ANFIS model is higher. View Full-Text
Keywords: unfrozen water; neural network; BPNN; ANFIS; NMR unfrozen water; neural network; BPNN; ANFIS; NMR
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Liu, Y.; Wang, Q.; Zhang, X.; Song, S.; Niu, C.; Shangguan, Y. Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China. Symmetry 2019, 11, 16.

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