Study on an AHP-Entropy-ANFIS Model for the Prediction of the Unfrozen Water Content of Sodium-Bicarbonate-Type Salinization Frozen Soil

Qing Wang, Yufeng Liu, Xudong Zhang, Huicheng Fu, Sen Lin, Shengyuan Song and Cencen Niu
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Qing Wang: College of Construction Engineering, Jilin University, Changchun 130026, China
Yufeng Liu: College of Construction Engineering, Jilin University, Changchun 130026, China
Xudong Zhang: Department of Civil Engineering, Shanghai University, Shanghai 200444, China
Huicheng Fu: Jilin Province Water Resource and Hydropower Consultative Company of P.R.CHINA, Changchun 130012, China
Sen Lin: Jilin Province Water Resource and Hydropower Consultative Company of P.R.CHINA, Changchun 130012, China
Shengyuan Song: College of Construction Engineering, Jilin University, Changchun 130026, China
Cencen Niu: College of Construction Engineering, Jilin University, Changchun 130026, China

Mathematics, 2020, vol. 8, issue 8, 1-20

Abstract: The development of agriculture and ecology, and the construction of water conservancy facilities are seriously hindered by the salinization of seasonal frozen soil. Unfrozen water exists in the freezing and thawing of frozen soil. This unfrozen water is the core and foundation for studying the process of seasonal frozen soil salinization. However, it is difficult to obtain the unfrozen water content (UW) in routine experiments, and it shows nonlinear characteristics under the action of the main factors contained: salt content, water content, and temperature. In this paper, a new model is proposed to predict the UW of saline soil based on the combined weighting method and the adaptive neuro-fuzzy inference system (ANFIS). Firstly, the distance function was used to combine the analytic hierarchy process (AHP) with the entropy weight method (the combined weighting method) to determine the importance of the influencing factors (temperature, initial water content, and salt content) on UW. On this basis, the AHP, entropy weight method, and adaptive neuro-fuzzy inference system (AHP-entropy-ANFIS) ensemble model was established. Secondly, the five-fold cross-validation method and statistical factors (coefficient of determination, mean squared error, mean absolute percent error, and mean absolute error) were applied to evaluate and compare the AHP-entropy-ANFIS ensemble model, the ANFIS model, the support vector machine (SVM) model, and the AHP, entropy weight method, and support vector machine (AHP-entropy-SVM) ensemble model. In addition, the prediction values of the four models and the experimental values were also compared. The results show that the AHP-entropy-ANFIS model had the strongest prediction capability and the best stability, and so is more suitable for predicting the UW of saline soil. This study provides useful guidance for preventing and mitigating salinization hazards in seasonally frozen areas.

Keywords: unfrozen water; AHP; entropy; SVM; ANFIS; saline soil; frozen soil (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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