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

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.

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“…The advantages and limitations of the entropy method in multi-objective optimization problems are presented by Kumar et al [47] The entropy method has been widely used in various fields. For example, Hafezalkotob developed the Shannon entropy-MULTIMOORA integration method for materials selection problems [48]. The AHP-entropy-ANFIS model had been established for predicting the unfrozen water of saline soil by Wang et al [49].…”

Unfrozen Water Content Estimation: A Comparison between Ensemble and Non-ensemble Machine Learning Models

“…Based on experimental data obtained by nuclear magnetic resonance, Liu et al (2018) constructed two models using adaptive network fuzzy inference system (ANFIS) and BPNN to predict the UWC of saline soil. Wang Q et al (2020) proposed a new model to predict the UWC of saline soil based on the combined weighting method and ANFIS. Ren Z et al (2023) established a model based on the genetic algorithm and BPNN to predict UWC under extremely-low-temperature conditions.…”