AUTHOR=Geng Qingtian , Wang Leilei , Li Qingliang TITLE=Soil temperature prediction based on explainable artificial intelligence and LSTM JOURNAL=Frontiers in Environmental Science VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2024.1426942 DOI=10.3389/fenvs.2024.1426942 ISSN=2296-665X ABSTRACT=

Soil temperature is a key parameter in many disciplines, and its research has important practical significance. In recent years, the prediction of soil temperature by deep learning has achieved good results. However, deep learning is difficult to popularize in practical use because of its opacity. This study aims to interpret and analyze the Long Short Term Memory Network (LSTM) model for global soil temperature prediction using SHapley Additive exPlanation (SHAP), Permutation Importance (PI) and Partial Dependence Plot (PDP). The results show that Temperature of air at 2 m above the surface of land has the greatest influence on the prediction of soil temperature, and its SHAP and PI characteristic values have significant seasonality. Meanwhile, radiation also has a certain influence on the prediction results. There was a significant positive correlation between the temperature of 2 m and the soil temperature. The explanatory insights provided in this paper enhance the transparency and confidence of the model, which promotes the applicability of soil temperature prediction models in relevant fields.