大理大学学报 ›› 2024, Vol. 9 ›› Issue (12): 36-45.

• 数学与计算机科学 • 上一篇    下一篇

云南省区域气候复杂层级网络建模与仿真分析

  

  1. (1.大理大学数学与计算机学院,云南大理 671003; 2.大理大学云南省汪景琇院士工作站,
    云南大理 671003; 3.广东工商职业技术大学人工智能与大数据学院,广东肇庆 526020)
  • 收稿日期:2023-05-15 出版日期:2024-12-15 发布日期:2024-12-17
  • 通讯作者: 罗桂兰,教授,博士,E-mail:yongxin_fly@163.com。
  • 作者简介:马欣,硕士研究生,主要从事复杂网络与大数据分析研究。
  • 基金资助:
    国家自然科学基金项目(61661001);云南省汪景琇院士工作站项目(202005AF150025)

Modeling and Simulation Analysis of Regional Climate Complex Hierarchical Network in Yunnan Province

  1. (1. College of Mathematics and Computer, Dali University, Dali, Yunnan 671003, China; 2. Academician Wang Jingxiu Workstation,
    Dali University, Dali, Yunnan 671003, China; 3. College of Artificial Intelligence and Big Data, Guangdong Vocational and
    Technical University of Business and Technology, Zhaoqing, Guangdong 526020, China)
  • Received:2023-05-15 Online:2024-12-15 Published:2024-12-17

摘要: 为解决云南省气候变化对人类生产、生活和生态环境造成的破坏性影响,了解该地区气候变化的主要影响因素,本研
究基于云南省1984—2016年32个气象站点的气候数据,利用复杂网络理论将气象站点抽象为网络节点,皮尔逊相关系数确定
边的连接权重,构建云南省单层和层级的气候复杂网络模型,分析网络特征参数和稳定性。研究发现:在单层网络中,气温、气
压、相对湿度和风速网络相关性较强,降水、日照网络相关性较弱,根据主成分分析法与单层网络实验结果得到气温、气压、相
对湿度为云南省气候变化的主要影响因子;在层级网络中,气温与气压、相对湿度、降水具有较强的负相关性,气温与风速、日
照主要呈现正中强相关,各气象要素间存在内在的关联性,删除气温、相对湿度、气压这3个气象因子对整个网络稳定性破坏
较大。因此建议从气温、气压、相对湿度这3个主要影响因子方面保护云南省的气候环境。

关键词: 复杂网络, 气候网络, 层级网络, 网络建模, 稳定性

Abstract: To solve the destructive impact of climate change in Yunan Province on human production, life, and the ecological environment and to understand the main influencing factors of climate change in this area, this study uses the complex network theory to abstract meteorological stations into network nodes based on the climate data of 32 meteorological stations in Yunnan Province from
1984 to 2016. The Pearson correlation coefficient is used to determine the connection weight of edges. A single-layer and hierarchical
climate complex network model in Yunnan Province are constructed, and the network characteristic parameters and stability are
analyzed. The research finds that in single-layer networks, the correlation between temperature, pressure, relative humidity, and wind
speed networks are strong, while the correlation between precipitation and sunlight networks is weak. According to the results of
principal component analysis and single-layer network experiments, temperature, pressure, and relative humidity are the main
influencing factors of climate change in Yunnan Province. In the hierarchical network, temperature has a strong negative correlation
with pressure, relative humidity, and precipitation, temperature mainly shows a strong positive correlation with wind speed and
sunlight. There is an inherent correlation between various meteorological elements. Deletion of the three meteorological factors of
temperature, relative humidity and pressure would significantly damage the stability of the entire network. Therefore, it is recommended to protect the climate and environment of Yunnan Province from these three main influencing factors.

Key words:  complex network, climate network, hierarchical network, network modeling, stability

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