地球学报2025，Vol.46Issue(5)：991-1006,16.DOI:10.3975/cagsb.2025.082703
基于机器学习的中国铁矿石资源产业链评估
Evaluation of China's Iron Ore Resource Industry Chain Based on Machine Learning
摘要
Abstract
Due to the low grade and insufficient supply of domestic iron ore resources,China's iron smelting production is highly dependent on imported iron ore.Therefore,it is particularly important to scientifically assess the security situation of China's iron ore resources.This study quantitatively evaluates the supply security of China's iron ore resources,provides real-time early warnings,and proposes corresponding strategies and measures for existing risk issues.First,relevant indicators are selected and constructed from the aspects of iron ore security guarantee,economic development,transportation turnover,economic risk,and price fluctuation factors.Second,the CatBoost algorithm is selected,optimized through Bayesian optimization using machine learning methods to predict the supply security coefficient of iron ore resources,and compared with other machine learning methods.Finally,the SHAP method is used to explain the model results and compare and analyze the contribution levels of each factor coefficient to the security of the iron ore industry and supply chains.Results show that the BO-Catboost model is more effective in predicting the supply coefficient than other machine learning methods.In the ranking of the characteristics of the factors influencing the iron ore coefficient,the economic risk-related indicators have the largest weight,followed by those of the economic development and transportation turnover factors,while the price fluctuation factors have the smallest indicator weight.Between 2012 and 2024,the economic uncertainty index,proportion of loss-making enterprises in the ferrous metal smelting and rolling processing industry,USD-CNY exchange rate,and iron ore inventory were the main aspects that affected the supply security of iron ore resources.China's iron ore resource supply faces many risks and challenges,among which the economic environment risk has the most significant impact.To maintain market stability and supply security,it is necessary to flexibly adjust the import tariffs and import rhythm of iron ore according to the international market supply and demand situation and the level of economic development.Based on relevant risk indicators,this study provides policy suggestions for the government and enterprises to optimize the allocation of iron ore resources and deal with economic environment risks.关键词
铁矿石资源供应/特征分析/SHAP模型Key words
iron ore resources supply/feature analysis/SHAP分类
矿业与冶金引用本文复制引用
李林泰,江飞涛,李海玲,谢聪敏,张艳飞,邵留国..基于机器学习的中国铁矿石资源产业链评估[J].地球学报,2025,46(5):991-1006,16.基金项目
本文由国家科技重大专项(编号:2024ZD1002002)、国家自然科学基金项目(编号:72373160 （编号:2024ZD1002002）
72203059)、教育部人文社科项目(编号:22YJCZH078)和国家社科基金重大项目(编号:22&ZD098)联合资助. This study was supported by the National Sci-ence and Technology Major Project(No.2024ZD1002002),National Natural Science Founda-tion of China(Nos.72373160 and 72203059),Hu-manities and Social Sciences Project of the Ministry of Education(No.22YJCZH078),and Philosophy and Social Science Foundation of China(No.22&ZD098). （编号:22YJCZH078）
