| 引用本文: | 张明虎,张妍,张中琼,达虎,李亮,贾玉诚.基于增强回归树的东北地区冻土分布[J].哈尔滨工业大学学报,2026,58(3):173.DOI:10.11918/202309006 |
| ZHANG Minghu,ZHANG Yan,ZHANG Zhongqiong,DA Hu,LI Liang,JIA Yucheng.Permafrost distribution in northeast China based on boosted regression tree[J].Journal of Harbin Institute of Technology,2026,58(3):173.DOI:10.11918/202309006 |
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| 基于增强回归树的东北地区冻土分布 |
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张明虎1,张妍1,张中琼2,达虎3,李亮1,贾玉诚1
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(1.兰州理工大学 计算机与通信学院, 兰州 730000; 2.中国科学院西北生态环境资源研究院,冰冻圈科学与冻土工程全国重点实验室, 兰州 730000; 3.甘肃省计算中心, 兰州 730000)
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| 摘要: |
| 中国东北地区多年冻土是兴安（外）贝加尔型多年冻土的重要组成部分,兼具高纬度和高海拔冻土特征。多年冻土的存在和变化,对区域寒区生态环境、水碳循环、寒区工程设计与运行等均会产生直接影响。目前,基于热边界条件的经验模型、半经验模型对多年冻土的分布面积存在高估现象,且对气温以外的环境因素影响考虑不足。为更精准刻画区域多年冻土分布,在区域调查和数据耦合的基础上,获取影响区域多年冻土分布的地带性与非地带性因素的空间变化特征,采用增强回归树模型进行模拟分析。结果表明:地带性因素中,纬度、经度与海拔的贡献度分别为45.3%、42.4%、12.3%；非地带性因素中:气温（包含冻结指数和融化指数）、降水、水土条件、积雪与植被的贡献度分别为46.4%、18.9%、13.1%、12.5%、9.1%。明晰了环境因素对兴安（外）贝加尔型多年冻土发育和变化的贡献。与分类回归决策树作对比,增强回归树模型分类精度达0.91。该研究为区域冻土和相关领域研究提供了数据支持和参考。 |
| 关键词: 兴安（外）贝加尔型多年冻土 增强回归树 环境因素 贡献度 多年冻土分布 |
| DOI：10.11918/202309006 |
| 分类号:TP399 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(2,4)；冰冻圈科学与冻土工程全国重点实验室(CSFSE-FX-2504);中国电力建设集团有限公司科技项目 (DJ-ZDXM-2023-35) |
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| Permafrost distribution in northeast China based on boosted regression tree |
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ZHANG Minghu1,ZHANG Yan1,ZHANG Zhongqiong2,DA Hu3,LI Liang1,JIA Yucheng1
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(1.School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730000, China; 2.State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; 3.Gansu Computing Center, Lanzhou 730000, China)
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| Abstract: |
| The permafrost in northeastern China constitutes an important component of the Xing’an-(trans)Baikal permafrost, exhibiting characteristics of both high latitude and high altitude permafrost. The presence and dynamics of permafrost directly impact the ecological environment of the regional cold zone, water-carbon cycles, cold region engineering design, and operations. Currently, empirical and semi-empirical models based on thermal boundary conditions tend to overestimate the areal extent of permafrost and insufficiently consider environmental factors beyond temperature. To more accurately delineate the regional distribution of permafrost, this paper obtained the spatial variation characteristics of zonal and non-zonal factors influencing regional permafrost distribution through regional surveys and data integration and employed the boosted regression tree model for simulation and analysis. The results indicate that among the zonal factors, latitude, longitude, and altitude contribute 45.3%, 42.4%, and 12.3%, respectively. Among the non-zonal factors, temperature (including freezing and thawing indices), precipitation, soil-water conditions, snow cover, and vegetation contribute 46.4%, 18.9%, 13.1%, 12.5%, and 9.1%, respectively. This analysis clarifies the contributions of environmental factors to the development and dynamics of Xing’an-(trans)Baikal permafrost. Compared with a classification and regression decision tree, the boosted regression tree model achieves an accuracy of 0.91. This study provides data support and reference for regional permafrost research and related fields. |
| Key words: Xing’an-(trans)Baikal permafrost boosted regression tree environmental factor contribution permafrost distribution |
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