Prediction of the Freezing Temperature of Saline Soil Using Neural Network Methods

Abstract

Freezing temperature is an important physical index of saline soil in permafrost and seasonal frozen area, and it is difficult to be predicted with a formula when saline soil contains multiple salts. In this study, we used a backpropagation neural network (BPNN) and a radial basis function neural network (RBFNN) to predict the freezing temperature of saline soil from the Qinghai–Tibet Plateau and Lanzhou. Several variables (ion content, soluble salt content, and water content) were adopted based on previous studies and experimental conditions. After the above two neural network models were established, the parameters were input into the two models to obtain the predicted values of the freezing temperature. Then, the measured and predicted values were compared to evaluate the accuracy of the two neural network models. Additionally, three statistical indicators were used to quantify the reliability of the two neural networks. Our results showed that BPNN had a stronger ability to predict freezing temperatures. Moreover, the established BPNN model was applied to analyze the sensitivity of the freezing temperature to the content of different ions under two different water content conditions. Finally, it was concluded that the influence of main ions on the freezing temperature in descending order was Cl− > K+ ≈ Na+ > SO42− > CO32− > Ca2+ under the condition of 10% water content, and K+ >Cl− > SO42− > Na+ > CO32− > Ca2+ when the water content was 30%. This study offers a new prediction method for the freezing temperature of multicomponent saline soil and can be used as a reference to investigate the factors affecting freezing temperatures.