| Summary: | 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.
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