合肥工业大学校徽

合肥工业大学学报自科版

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

主管：中华人民共和国教育部

主办：合肥工业大学

ISSN 1003-5060, CN 34-1083/N

基于大数据和熵权-随机森林的城市地下空间需求评价

Urban underground space demand evaluation based on big data and entropy-random forest

期刊信息

合肥工业大学（自然科学版），2025年3月，第48卷第3期：360-368

DOI: 10.3969/j.issn.1003-5060.2025.03.011

作者信息

葛睿雅，李晓晖，袁峰，窦帆帆，熊芸莹，薛晨

（合肥工业大学资源与环境工程学院，安徽合肥230009）

摘要和关键词

摘要: 科学评估地下空间开发需求潜力是缓解城市化问题和合理拓展有限区域的重要基础工作。目前地下空间评价中的社会经济数据多来自于传统官方文件, 其全面完整性和时空精度并不理想; 此外主客观赋权方法的使用, 一定程度上存在主观性强和受数据干扰等不足。文章以多源大数据支持的指标体系为基础, 构建熵权-随机森林耦合的地下空间需求评价模型。该模型基于熵权法确定负样本, 将总样本和指标因子导入随机森林算法中, 挖掘社会经济指标与现有地下设施间的复杂非线性关系。研究表明, 经过网格搜索调优后的模型 AUC(area under curve) 精度达到 0.979, 其中 77.45% 的现有设施落入评价的高需求区内, 证明所采用模型有较强的准确性和可靠性, 其精细化评价结果可为今后地下建设选址提供更符合实际的借鉴。

关键词: 熵权-随机森林模型；多源地理大数据；社会经济指标因子；地下空间需求评价

Authors

GE Ruiya, LI Xiaohui, YUAN Feng, DOU Fanfan, XIONG Yunying, XUE Chen

(School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: Scientific assessment of underground space development demand potential is an important basic work to alleviate urbanization problems and reasonably expand limited areas. The current socioeconomic data in underground space evaluation are mostly from traditional official documents, and their comprehensive completeness and spatio-temporal accuracy are not ideal. In addition, the use of subjective and objective assignment methods is to a certain extent subjective and subject to data interference. This paper constructs an entropy-random forest coupled underground space demand evaluation model based on the index system supported by multi-source big data. The model is based on the entropy weight method to determine the negative sample, and the total sample and index factors are imported into the random forest algorithm to explore the complex nonlinear relationship between socio-economic indices and existing underground facilities. The case study shows that the area under curve (AUC) accuracy of the model after grid search tuning reaches 0.979, in which 77.45% of the existing facilities fall into the high demand area of the evaluation, which proves that the adopted model has strong accuracy and reliability, and its refined evaluation results can be used as a practical reference for future underground construction site selection.

Keywords: entropy-random forest model; multi-source geographic big data; socio-economic index factors; underground space demand evaluation

基金信息

安徽省自然科学基金资助项目（1808085QD116）；安徽省公益性地质调查工作资助项目（2023-g-1-18）和中央高校基本科研业务费专项资金资助项目（PA2019GDZC0093）

前一篇后一篇

返回期刊目录