Prediction of thermal conductivity of natural rock materials using LLE-transformer-lightGBM model for geothermal energy applications

作者：Yufan Wang, Tianxing Ma, Liangxu Shen, Xu Wang, Rui Luo · 发表于：Energy Reports · 年份：2025 · DOI：10.1016/j.egyr.2025.02.003 · 被引用次数：29 · 研究领域：Geothermal Energy Systems and Applications、Hydrocarbon exploration and reservoir analysis、Radiative Heat Transfer Studies

This paper focuses on the critical issue of predicting rock thermal conductivity in geothermal development and underground engineering, proposing a hybrid prediction method combining LLE (Locally Linear Embedding), Transformer, and LightGBM. The model leverages LLE for feature dimensionality reduction, Transformer for deep feature learning, and LightGBM for efficient regression analysis, achieving accurate and efficient predictions under complex and limited data conditions. Granite is selected as the study subject, and a multivariate database incorporating chemical compositions, physical properties, and environmental factors is constructed. An innovative inverse decomposition method is proposed to quantify the importance of original features during dimensionality reduction and prediction, revealing the significant impact of key factors such as surface distance, SiO₂, and CaO on thermal conductivity . Experimental results show that the proposed model significantly outperforms traditional methods in prediction accuracy, error control, and generalization capability, achieving an R² of 0.952, RMSE of 0.086, and MAE of 0.0739. A comparative analysis further demonstrates that the LLE-Transformer-LightGBM model outperforms other machine learning approaches, including Support Vector Regression (R² = 0.821, RMSE = 0.141) and Back Propagation Neural Network (R² = 0.860, RMSE = 0.148). These results highlight the superior predictive accuracy and robustness of the proposed model in handl...