摘要
准确预测浅层地下温度对于降低投资风险和推动浅层地热能开发利用具有重要意义。本研究基于粒子群优化(PSO)和极限梯度提升(XGB)的混合模型(PSO-XGB),并将其与K近邻(KNN)、支持向量回归(SVR)、随机森林(RF)和极限梯度提升(XGB)等单一模型进行了比较。首先收集了54组钻孔数据,使用克里金插值法对数据集进行扩充,经过相关性分析最终选择经纬度坐标、年平均降雨量、年平均气温和与断裂距离等因素用作预测100 m地下温度的输入特征。然后利用测试集对预测模型进行验证,使用均方根误差(E_(RMS))、平均绝对误差(E_(MA))、决定系数(R^(2))和均方误差(EMS)等指标评估了模型的性能。结果表明,PSO-XGB混合模型在测试集表现最好,ERMS为0.0706,E_(MA)值为0.0549,R^(2)值为0.9620,E_(MS)值为0.0050,在精度和拟合程度上明显高于其他模型,可知PSO-XGB混合模型在预测性能方面优于单一模型。
Accurate prediction of shallow underground temperature is of great significance for reducing investment risks and promoting the development and utilization of shallow geothermal energy.In this study,a hybrid model based on particle swarm optimization(PSO)and extreme gradient boosting(PSO-XGB)was developed and compared with single models including K-nearest neighbors(KNN),support vector regression(SVR),random forest(RF),and extreme gradient boosting(XGB).Firstly,54 sets of borehole data were collected,and the dataset was expanded using Kriging interpolation.Latitude and longitude coordinates,annual average rainfall,annual average temperature,and distance to faults were used as input features for predicting the temperature at a depth of 100 meters underground.Then the performance of the models was evaluated using metrics such as root mean squared error,mean absolute error,coefficient of determination,andmean squared error.The results showed that the PSO-XGB hybrid model outperformed the single models in terms of predictive performance.The E_(RMS)is 0.0706,the E_(MA)is 0.0549,the R^(2)is 0.9620,and the E_(MS)is 0.0050,which is significantly higher than the other models in terms of precision and degree of fitting.Therefore,the PSO-XGB hybrid model is superior to the single model in prediction performance.
作者
于子望
郑天琪
程钰翔
Yu Ziwang;Zheng Tianqi;Cheng Yuxiang(College of Construction Engineering,Jilin University,Changchun 130026,China;Key Laboratory of Groundwater Resources and Environment(Jilin University),Ministry of Education,Changchun 130021,China;Engineering Research Center of Geothermal Resources Development Technology and Equipment(Jilin University),Ministry of Education,Changchun 130021,China)
出处
《吉林大学学报（地球科学版）》
CAS
CSCD
北大核心
2023年第6期1907-1916,共10页
Journal of Jilin University：Earth Science Edition
基金
国家重点研发计划项目(2019YFC0604905)
中国博士后科学基金项目(2022M711291)。
