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Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China

文献类型：期刊论文

作者Dong, Jianhua1; Huang, Guomin1; Wu, Lifeng1,2; Liu, Fa3; Li, Sien4; Cui, Yaokui5; Wang, Yicheng2; Leng, Menghui6; Wu, Jie7; Wu, Shaofei1
刊名APPLIED SCIENCES-BASEL
出版日期2022-05-01
卷号12期号:10页码:26
关键词soil temperature machine learning models climatic zones extreme gradient boosting principal components analysis
DOI10.3390/app12105088
通讯作者Wu, Lifeng(wulifeng@nit.edu.cn)
英文摘要Accurate estimation of soil temperature (T-s) at a national scale under different climatic conditions is important for soil-plant-atmosphere interactions. This study estimated daily T-s at the 0 cm depth for 689 meteorological stations in seven different climate zones of China for the period 1966-2015 with the M5P model tree (M5P), random forests (RF), and the extreme gradient boosting (XGBoost). The results showed that the XGBoost model (averaged coefficient of determination (R-2) = 0.964 and root mean square error (RMSE) = 2.066 degrees C) overall performed better than the RF (averaged R-2 = 0.959 and RMSE = 2.130 degrees C) and M5P (averaged R-2 = 0.954 and RMSE = 2.280 degrees C) models for estimating T-s with higher computational efficiency. With the combination of mean air temperature (T-mean) and global solar radiation (R-s) as inputs, the estimating accuracy of the models was considerably high (averaged R-2 = 0.96-0.97 and RMSE = 1.73-1.99 degrees C). On the basis of T-mean, adding R-s to the model input had a greater degree of influence on model estimating accuracy than adding other climatic factors to the input. Principal component analysis indicated that soil organic matter, soil water content, T-mean, relative humidity (RH), R-s, and wind speed (U-2) are the main factors that cause errors in estimating T-s, and the total error interpretation rate was 97.9%. Overall, XGBoost would be a suitable algorithm for estimating T-s in different climate zones of China, and the combination of T-mean and R-s as model inputs would be more practical than other input combinations.
WOS关键词ARTIFICIAL NEURAL-NETWORK ; GLOBAL SOLAR-RADIATION ; REFERENCE EVAPOTRANSPIRATION ; MOISTURE ; DYNAMICS ; SCALE ; RESPIRATION ; DEPENDENCE ; PLATEAU ; ZONES
资助项目National Natural Science Foundation of China[51709143] ; Jiangxi Natural Science Foundation of China[20181BBG78078] ; Jiangxi Natural Science Foundation of China[20212BDH80016]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000802751600001
出版者MDPI
资助机构National Natural Science Foundation of China ; Jiangxi Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/178520]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Lifeng
作者单位1.Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China
2.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
4.China Agr Univ, Ctr Agr Water Res China, Beijing 100083, Peoples R China
5.Peking Univ, Sch Earth & Space Sci, Inst RS & GIS, Beijing 100871, Peoples R China
6.Nanchang Inst Technol, Jiangxi Key Lab Hydrol Water Resources & Water En, Nanchang 330099, Jiangxi, Peoples R China
7.Wuhan Polytech Univ, Sch Civil Engn & Architecture, Wuhan 430023, Peoples R China
推荐引用方式
GB/T 7714		 Dong, Jianhua,Huang, Guomin,Wu, Lifeng,et al. Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China[J]. APPLIED SCIENCES-BASEL,2022,12(10):26.
APA Dong, Jianhua.,Huang, Guomin.,Wu, Lifeng.,Liu, Fa.,Li, Sien.,...&Wu, Shaofei.(2022).Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China.APPLIED SCIENCES-BASEL,12(10),26.
MLA Dong, Jianhua,et al."Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China".APPLIED SCIENCES-BASEL 12.10(2022):26.

入库方式： OAI收割

来源：地理科学与资源研究所

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