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

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成果类型:
期刊论文
作者:
Dong, Jianhua;Huang, Guomin;Wu, Lifeng;Liu, Fa;Li, Sien;...
通讯作者:
Lifeng Wu
作者机构:
[Dong, Jianhua; Huang, Guomin; Wu, Lifeng; Wu, Shaofei] Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China.
[Wu, Lifeng; Wang, Yicheng] China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China.
[Liu, Fa] Chinese Acad Sci, Inst Geog Sci & Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China.
[Li, Sien] China Agr Univ, Ctr Agr Water Res China, Beijing 100083, Peoples R China.
[Cui, Yaokui] Peking Univ, Sch Earth & Space Sci, Inst RS & GIS, Beijing 100871, Peoples R China.
通讯机构:
[Lifeng Wu] S
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China<&wdkj&>School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
soil temperature;machine learning models;climatic zones;extreme gradient boosting;principal components analysis
期刊:
Applied Sciences-Basel
ISSN:
2076-3417
年:
2022
卷:
12
期:
10
页码:
5088-
基金类别:
Conceptualization, J.D. and L.W.; methodology, J.D. and L.W.; software, L.W.; validation, G.H., J.D. and L.W.; formal analysis, J.D. and L.W.; investigation, F.L., S.L., and Y.C.; data curation, J.D. and L.W.; writing—original draft preparation, J.D.; writing—review and editing, L.W. and G.H.; visualization, Y.W., M.L., J.W. and S.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 51709143, and Jiangxi Natural Science Foundation of China (No. 20181BBG78078 and 20212BDH80016).
机构署名:
本校为其他机构
院系归属:
土木工程与建筑学院
摘要:
Accurate estimation of soil temperature (Ts) at a national scale under different climatic conditions is important for soil–plant–atmosphere interactions. This study estimated daily Ts 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 (R2) = 0.964 and root mean square error (RMSE) = 2.066◦C) overall p...

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