Prediction of soil thermal conductivity using artificial intelligence approaches

•Three AI models for predicting the soil thermal conductivity are proposed.•Compared the three AI models with five existing models.•A web page program for the proposed models was built. In this study, three artificial intelligence models, namely group method of data handing (GMDH), multi expression programming (MEP) and random forest (RF), are proposed to predict soil thermal conductivity. A large database of 444 data samples collected from existing literature was used to construct the three models. Three statistical indexes were used to evaluate the performance of the proposed three models and the other five existing models. The results show that the predicted results of the three models are in good agreement with the experimental results, and the RF model has the best comprehensive performance. Therefore, RF model is recommended to predict the thermal conductivity of unsaturated soil. To facilitate practical engineering applications, we have built a web program for the three proposed models, and users can use these three models by visiting the website.