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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1426942

Soil temperature prediction based on Explainable Artificial Intelligence and LSTM

Provisionally accepted
Qingtian Geng Qingtian Geng Leilei Wang Leilei Wang Qingliang Li Qingliang Li *
  • Changchun Normal University, Changchun, Jilin Province, China

The final, formatted version of the article will be published soon.

    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 2m 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 meters 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.

    Keywords: deep learning, soil temperature, XAI, Shap, Permutation importance, Partial dependence plot

    Received: 03 May 2024; Accepted: 17 Jul 2024.

    Copyright: © 2024 Geng, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qingliang Li, Changchun Normal University, Changchun, 130032, Jilin Province, China

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