| 基于支持向量机的京津冀城市群热环境时空形态模拟 |
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| 引用本文： | 杨浩,孟娜,王婧,郑燕,赵莉.基于支持向量机的京津冀城市群热环境时空形态模拟[J].地球信息科学,2019,21(2):190-200. |
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| 作者姓名： | 杨浩 孟娜 王婧 郑燕 赵莉 |
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| 作者单位： | 1. 北京市社会科学院,北京 1001012. 北京大学经济学院,北京 1008713. 北京市延庆区交通局,北京 1021004. 北京师范大学政府管理学院,北京 1008755. 河北民族师范学院外语学院,承德 0670006.西南科技大学文学与艺术学院,绵阳 6210007. 复旦大学,上海 200433 |
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| 基金项目： | 国家社会科学基金项目（18CGL048）;国家自然科学基金重点项目（41731286）;中国博士后科学基金资助项目（2018M631383）;北京市社会科学院青年项目（2018B5186） |
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| 摘 要： | 城市群热环境作为区域生态重要组成部分,已成为近年来的研究热点。而如何选择针对城市群这种复杂地地貌特征的热环境量化工具一直是亟待解决的技术难点,基于此本研究提出了一种解决多样本、非线性、非平稳及高维函数拟合的计算方法,并建立了基于支持向量机(SVM)的京津冀城市群热环境曲面模型来揭示城市群热环境的时空形态变化。研究结果表明：① SVM模型在刻画多核心、多种土地利用类型城市群热环境的空间分布方面具有理论与实践可行性,能够根据热环境的整体空间布局通过高斯核函数进行局部优化差值,最大限度减少缺省值对模型拟合结果的影响。相比于对照方法可以模拟出更高精度的复杂地貌特征城市群热岛空间分布格局;② 在SVM模型曲面拟合的过程中,拟合精度和拟合时间是衡量拟合结果的重要指标,而原始影像的分辨率则是影响该指标的决定性因素;③ 2003-2013年区域内北京市与天津市的城市热岛效应变化最为明显,热岛面积分别增加7091 km2与4196 km2,空间上呈现出逐年接近连片发展趋势,热岛重心移动轨迹具有明显的时空分异性。北京城市热岛特征为东南部地区异速增长,西部地区缓慢增长;天津城市热岛特征为以城市中心为圆心向周围扩展。本研究进一步丰富了城市群热环境评测的定量方法,可以在实践上对城市群的城市规划、城市建设、环境保护和区域可持续发展等提供定量化、可视化的决策支持。
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| 关 键 词： | 城市热环境 时空形态 高斯曲面模型 支持向量机(SVM) 京津冀城市群 |
| 收稿时间： | 2018-06-04 |
Spatial-temporal Morphology Simulation of Beijing-Tianjin-Hebei Urban Agglomeration Thermal Environment based on Support Vector Machine |
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| Hao YANG,Na MENG,Jing WANG,Yan ZHENG,Li ZHAO.Spatial-temporal Morphology Simulation of Beijing-Tianjin-Hebei Urban Agglomeration Thermal Environment based on Support Vector Machine[J].Geo-information Science,2019,21(2):190-200. |
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| Authors: | Hao YANG Na MENG Jing WANG Yan ZHENG Li ZHAO |
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| Institution: | 1. Beijing Academy of Social Sciences, Beijing 100101, China2. School of Economics, Peking University, Beijing 100871, China3. Beijing Yanqing District Transportation Bureau,Beijing 102100, China4. School of Government, Beijing Normal University, Beijing 100875, China5. Hebei Normal University for Nationalities, Chengde 067000, China; School of Literature and Art, Southwest University of Science and Technology, Mianyang 621000, China7. Fudan University, Shanghai 200433, China |
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| Abstract: | As an important part of regional ecology, the thermal environment of urban agglomeration has become a research hot topic in recent years. How to choose the thermal environment quantification mehod for the complex geomorphological features of urban agglomeration has been a difficult technical problem to be solved. Based on this, This study proposes a solution to multi-sample, nonlinear, non-stationary and high-dimensional function fitting. The calculation method is established, and the thermal environment surface model of Beijing-Tianjin-Hebei urban agglomeration based on support vector machine (SVM) is established to reveal the temporal and spatial morphological changes of the thermal environment of urban agglomeration. The results show: ①that the SVM model has theoretical and practical feasibility in describing the spatial distribution of the thermal environment of urban agglomerations with multi-core and multi-land-use types. It can optimize the differences locally through the Gaussian Kernel Function according to the overall spatial distribution of the thermal environment, and minimize the impact of default values on the fitting results of the model. Comparing with the control method, the spatial distribution pattern of heat island in urban agglomerations with complex geomorphologic features can be simulated with higher accuracy. ② In the process of fitting the surface of SVM model, accuracy and the time of fitting are important indexes to measure the results, and original image resolution is the decisive influencing factor. ③ In 2003-2013, the most obviously change regions of urban heat island effect are Beijing and Tianjin. The heat island area of the two cities increased by 7091 km square and 4196 km square, respectively. The spatial trend was developing continually year by year, and the trajectory of gravity center of the heat island had obvious spatial and temporal variations. Beijing's urban heat island is characterized by uneven growth in the southeast and slow growth in the west, while Tianjin's urban heat island is characterized by the expansion of city center to the surrounding areas. This study further enriches the quantitative methods of urban thermal environment assessment, and can provide quantitative and visual decision supports for urban agglomeration planning, urban construction, environmental protection and regional sustainable development practically. |
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| Keywords: | urban thermal environment temporal and spatial patterns gaussian surface model Support Vector Machine (SVM) Beijing-Tianjin-Hebei urban agglomeration |
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