土壤热导率是影响地下温度分布的重要土壤性质，在岩土工程和土木工程施工设计中有重要意义，采用合理手段对其进行预测可有效解决测量耗时长、过程复杂等问题。针对土壤热导率数据非线性和时序性等特征，文中提出一种基于归一化互信息法（NMI）下的萤火虫算法（FA）优化极限学习机（DELM）预测模型（NMI-FA-DELM）进行土壤热导率预测。该模型首先通过NMI筛选影响土壤热导率的关键参数，并将筛选后的参数作为数据集，然后用萤火虫算法优化下的极限学习机（FA-DELM）对土壤热导率进行预测，并对比统计预测方程、随机森林方法、BP神经网络模型、DELM模型、SVR（支持向量回归）模型的预测结果。研究结果表明，NMI-FA-DELM模型能有效预测土壤热导率，预测结果对应的均方根误差、平均绝对百分比误差、a10指数和决定系数分别为0.363、9.667%、0.961和0.92，其预测效果较其他预测模型更好，粘性土含量、含沙量对土壤热导率预测结果影响较大。NMI-FA-DELM模型可有效提高土壤热导率预测精度，对实际工程中预测土壤热导率有重要的指导意义。

The thermal conductivity of soil is an important soil property, which affects the soil temperature distribution underground. It has significant practical importance in geotechnical and civil engineering design and construction. Using reasonable means to predict it can effectively solve the problems such as time-consuming and complex process. According to the characteristics of nonlinearity and timing of soil thermal conductivity data, this paper proposed a firefly algorithm (FA) optimization limit learning machine (DELM) prediction model (NMI-FA-DELM) under NMI for soil thermal conductivity prediction. The model first screened the key parameters affecting the soil thermal conductivity by NMI, and took the filtered parameters as the data set. Then the soil thermal conductivity was predicted with the FA-DELM optimized by the firefly algorithm, and the predictive results were compared with those of statistical prediction equations, random forest methods, BP neural network models, DELM models, and SVR (support vector regression) models. The results show that the NMI-FA-DELM model can effectively predict soil thermal conductivity, with corresponding root mean square error, average absolute percentage error, a10 index, and determination coefficient of 0.363, 9.667%, 0.961 and 0.92, respectively. The prediction accuracy of the NMI-FA-DELM model is better than that of other prediction models, and the content of viscous soil and sand has greater influence on the prediction results of soil thermal conductivity. This model can significantly improve the prediction accuracy of soil thermal conductivity and provides important guidance for predicting soil thermal conductivity in practical engineering applications.