Analysis of the Driving Mechanism of Grassland Degradation in Inner Mongolia Grassland from 2015 to 2020 Using Interpretable Machine Learning Methods
文献类型：期刊论文
| 作者 | Zhang, Zuopei2,3; Hu, Yunfeng2,3; Batunacun1 |
| 刊名 | LAND
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 14期号:2页码:386 |
| 关键词 | machine learning grassland degradation driving factors SHAP method climate change |
| DOI | 10.3390/land14020386 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | In traditional studies on grassland degradation drivers, researchers often lacked the flexibility to selectively consider driving factors and quantitatively depict their contributions. Interpretable machine learning offers a solution to these challenges. This study focuses on Inner Mongolia, China, incorporating four categories and sixteen specific driving factors, and employing four machine learning techniques (Logistic Regression, Random Forest, XGBoost, and LightGBM) to investigate regional grassland changes. Using the SHAP approach, contributions of driving factors were quantitatively analyzed. The findings reveal the following: (1) Between 2015 and 2020, Inner Mongolia experienced significant grassland degradation, with an affected area reaching 12.12 thousand square kilometers. (2) Among the machine learning models tested, the LightGBM model exhibited superior prediction accuracy (0.89), capability (0.9), and stability (0.76). (3) Key factors driving grassland changes in Inner Mongolia include variations in rural population, livestock numbers, average temperatures during the growth season, peak temperatures, and proximity to roads. (4) In eastern and western Inner Mongolia, changes in rural population (31.4%) are the primary degradation drivers; in the central region, livestock number changes (41.1%) dominate; and in the southeast, climate changes (19.3%) are paramount. This work exemplifies the robust utility of interpretable machine learning in predicting grassland degradation and offers insights for policymakers and similar ecological regions. |
| URL标识 | 查看原文 |
| WOS关键词 | EXPLAINABLE ARTIFICIAL-INTELLIGENCE ; QUANTITATIVE ASSESSMENT ; VEGETATION COVER ; CLIMATE-CHANGE ; CHINA ; ALLOCATION ; PLATEAU ; BIOMASS |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001431033700001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213345]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Hu, Yunfeng |
| 作者单位 | 1.Inner Mongolia Normal Univ, Coll Geog Sci, Hohhot 010028, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Zuopei,Hu, Yunfeng,Batunacun. Analysis of the Driving Mechanism of Grassland Degradation in Inner Mongolia Grassland from 2015 to 2020 Using Interpretable Machine Learning Methods[J]. LAND,2025,14(2):386. |
| APA | Zhang, Zuopei,Hu, Yunfeng,&Batunacun.(2025).Analysis of the Driving Mechanism of Grassland Degradation in Inner Mongolia Grassland from 2015 to 2020 Using Interpretable Machine Learning Methods.LAND,14(2),386. |
| MLA | Zhang, Zuopei,et al."Analysis of the Driving Mechanism of Grassland Degradation in Inner Mongolia Grassland from 2015 to 2020 Using Interpretable Machine Learning Methods".LAND 14.2(2025):386. |
入库方式： OAI收割
来源：地理科学与资源研究所
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