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Machine learning with feature selection reveals key drivers of multi-depth soil moisture content

Qi Wu JiaXin Bian XiaoLi Wang XiaoYing Qiao Ning Wang Yue You

Research in Cold and Arid Regions2026，Vol.18Issue(3)：P.270-283,14.

Research in Cold and Arid Regions2026，Vol.18Issue(3)：P.270-283,14.DOI:10.1016/j.rcar.2025.09.007

Machine learning with feature selection reveals key drivers of multi-depth soil moisture content

Qi Wu ¹JiaXin Bian ¹XiaoLi Wang ²XiaoYing Qiao ¹Ning Wang ¹Yue You¹

作者信息

1. School of Water and Environment,Chang''an University,Xi''an,Shaanxi 710054,China Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education,Chang''an University,Xi''an,Shaanxi 710054,China Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources,Chang''an University,Xi''an,Shaanxi 710054,China
2. Uxin Banner Branch of Ordos Ecology and Environment Bureau,Uxin Banner,Inner Mongolia Autonomous Region 017000,China
折叠

摘要

关键词

Soil moisture content/Feature selection/Machine learning/Semi-arid region

分类

农业科技

引用本文复制引用

Qi Wu,JiaXin Bian,XiaoLi Wang,XiaoYing Qiao,Ning Wang,Yue You..Machine learning with feature selection reveals key drivers of multi-depth soil moisture content[J].Research in Cold and Arid Regions,2026,18(3):P.270-283,14.

基金项目

supported by grants from the Provincial Key R&D Program of Shaanxi(Grant No.2021ZDLSF05-03) （Grant No.2021ZDLSF05-03）

the Na-tional Natural Science Foundation of China(Grant No.42107067) （Grant No.42107067）

the China Postdoctoral Science Foundation(Grant No.2021M692745). （Grant No.2021M692745）

Research in Cold and Arid Regions

ISSN：2097-1583

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