Research on soil thermal conductivity prediction algorithm based on improved BP neural network

The measurement of soil thermal conductivity plays a key role in the soil heat source pump air conditioning system in the design area. In order to solve the problem that the measurement period of traditional soil thermal conductivity measurement is too long, a prediction model of soil thermal conductivity based on improved BP neural network learning algorithm is proposed. By collecting the average temperature in the U-pipe after 50 hours in different parts of the country, TRAINSCG (quantized conjugate gradient method), TRAINGD (gradient descent training function) and TRAINRP (elastic gradient descent method) were used to optimize and improve the prediction model to obtain the final prediction model of soil thermal conductivity, and the accuracy of the three improved algorithms was calculated by testing samples. The results show that the BP neural prediction model using TRAINSCG learning algorithm has the best prediction effect on soil thermal conductivity, the BP neural prediction model using TRAINSCG learning algorithm has a better prediction effect on soil thermal conductivity, and the BP neural network prediction model using TRAINGD learning algorithm has a poor prediction effect on soil thermal conductivity.