基于机器学习的采煤沉陷区耕地恢复力评价  

Assessment of the resilience of cropland in coal mining subsidence area based on machine learning

作　　者：金典 杨永均[2] 杨爽 陈赞旭 李丁[2] 张绍良[2] JIN Dian;YANG Yongjun;YANG Shuang;CHEN Zanxu;LI Ding;ZHANG Shaoliang(School of Resources and Geosciences,China University of Mining and Technology,Xuzhou 221116,China;Jiangsu Jiawang Resource Exhausted Mining Area Land Restoration and Ecological Succession,Ministry of Education Field Scientific Observation and Research Station,China University of Mining and Technology,Xuzhou 221116,China;School of Public Policy&Management,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]中国矿业大学资源与地球科学学院,江苏徐州221116 [2]中国矿业大学江苏贾汪资源枯竭矿区土地修复与生态演替教育部野外科学观测研究站,江苏徐州221116 [3]中国矿业大学公共管理学院,江苏徐州221116

出　　处：《中国矿业》2025年第4期190-197,共8页China Mining Magazine

基　　金：国家重点研发计划项目资助(编号:2023YFC3804202);国家自然科学基金项目资助(编号:52474197);教育部人文社会科学研究青年基金项目资助(编号:24YJC630272)。

摘　　要：耕地恢复力是耕地遭受外界扰动后恢复的能力,是土地复垦与生态修复规划编制的依据。采煤沉陷区耕地恢复力评估还缺少有效方法。本文引入机器学习方法,将恢复力评价转换为对耕地修复可能性概率的测算,并对徐州市城北采煤沉陷区耕地恢复力水平开展实证研究。结果表明:①随机森林算法可作为恢复力评价的新方法,模型优化后的平均精确度达88.28%,能够充分利用耕地修复的历史经验,避免评价过程中的主观性,准确反映采煤沉陷区耕地恢复的能力。②研究区耕地恢复力概率介于0.037~0.995之间,沉陷区外围地带恢复力高,中心区恢复力低。③机器学习表明,灌溉保证率是影响采煤沉陷区耕地恢复力的核心因子,重要性占比达到20.88%,积水深度次之,占比17.15%;土壤有机质含量、耕地破碎度和道路可达性等因子的影响也较大,重要性占比分别为15.18%、12.58%和12.38%。本研究表明利用训练样本数据和机器学习方法可以有效评估采煤沉陷区耕地恢复力水平,为矿区土地复垦和生态修复决策提供科学依据。The resilience of cropland refers to the ability of cropland to recovery after external disturbance,which is the basis for the determination of land reclamation and ecological restoration strategies.There is still a lack of effective methods to assess the resilience of cropland in coal mining subsidence area.In this paper,machine learning method is introduced to convert the resilience assessment into the measurement of the probability of the possibility of cropland restoration,and empirical research is carried out on the resilience level of cropland in coal mining subsidence area in north mining area of Xuzhou.The results show that random forest algorithm can be used as a new method of resilience assessment,and the accuracy of the model optimization is as high as 88.28%,which can make full use of the historical experience of cropland restoration,avoid subjectivity in the assessment process,and accurately reflect the ability of cropland restoration in coal mining subsidence area.The probability of resilience of cropland lies between 0.037 and 0.995,with high resilience in the peripheral zone of the subsidence area and low resilience in the central zone.The machine learning shows that irrigation guarantee rate is the core factor affecting the resilience of cropland in coal mining subsidence area,with an importance ratio of 20.88%;followed by the depth of waterlogging,with a ratio of 17.15%;the factors such as the content of soil organic matter,the fragmentation of cropland,and the accessibility to roads also have a great impact,with importance ratios of 15.18%,12.58%,and 12.38%,respectively.The study shows that the training sample data and machine learning method can effectively assess the level of resilience of coal mining subsided cropland,which provides a scientific reference for the decision-making of land reclamation and ecological restoration in mining areas.

关 键 词：生态修复 恢复力 机器学习 采煤沉陷 耕地 

分 类 号：TD88[矿业工程—矿山开采] X826[环境科学与工程—环境工程]