兰州大学机构库 >资源环境学院
使用机器学习和地表温度数据模拟城市热岛足迹
Alternative TitleSimulating urban heat island footprint using machine learning and land surface temperature data
孙敏淮
Subtype硕士
Thesis Advisor刘理臣
2020-11-26
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name工程硕士
Degree Discipline环境工程
Keyword地表城市热岛足迹 机器学习 模拟
Abstract城市热岛是当前城市环境、人居环境的热点研究问题之一。城市热岛足迹(SUHIF)是指城市升温(或降温)效应叠加在背景温度场上间接导致周围郊区地表温度(LST)改变的现象。当前针对SUHIF的模拟方法主要有两类:一类是侧重模拟足迹距离D的指数拟合法,另一类是侧重模拟整体空间形态的几何指标方法。 本文使用机器学习技术和地表温度数据对SUHIF进行模拟,旨在为SUHIF的模拟研究提供一种方法与思路借鉴。具体内容包括:①使用机器学习技术设计、构建并训练机器学习模型②分析比较机器学习模型与指数拟合模型的模拟能力③使用最佳机器学习模型对案例城市进行应用测试。 研究结果表明:①在现有数据及训练条件下,使用简单机器学习建模得到的最优模型为ANN(15-BR),而使用特征工程结合机器学习建模得到的最优模型为可优化kNN和ANN(10-LM)组成的串联模型。其中ANN(15-BR)是以15个神经元、贝叶斯正则化算法训练的人工神经网络模型可优化kNN是可优化k最邻近模型,其邻点个数为4,距离度量指标为相关性,距离权重为等距离ANN(10-LM)是以10个神经元、Levenberg-Marquardt算法训练的人工神经网络模型②机器学习技术对于提升足迹距离D的模拟精度效果明显,三种模型中使用特征工程和机器学习方法建立的模型综合模拟效果最佳③对12个案例城市的SUHIF形态可视化模拟显示,该机器学习模型具备良好的数据适用性,且模型可用于足迹形态空间精度的提升。
Other AbstractUrban heat island is one of the hot issues in the study of urban environment and human-settlement environment. The urban heat island footprint (SUHIF)is a phenomenon that the urban warming effect superimposes on the background temperature field and indirectly leads to the increase of land surface temperature (LST) in the surrounding suburbs. At present, there are two kinds of simulation methods for SUHIF: one is the exponential fitting method which emphasizes on the simulation of the footprint value(D), The other is the geometric index method which emphasizes on the simulation of the whole space shape. In this paper, we use machine learning technology and land surface temperature data to simulate SUHIF, in order to provide a method and ideas for SUHIF simulation. The specific contents include: (1) Designing, constructing and training machine learning models using machine learning technology(2) Analyzing and comparing the simulation performance of machine learning models and the exponential fitting model(3) Testing the application of the best machine learning model to the case city. The results show that: (1) Under the current data and training conditions, the optimal model based on simple machine learning is ANN (15-BR) , the optimal model based on feature engineering and machine learning is a series model composed of kNN and ANN(10-LM)(2) Machine learning technology has obvious effect on improving the simulation precision of footprint distance D, and the model based on feature engineering and machine learning is the best model among the three models(3) The visual simulation of 12 case cities shows that the machine learning model has good data applicability, and the model can be used to improve the spatial accuracy of footprint morphology.
Pages87
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/467620
Collection资源环境学院
Affiliation
资源环境学院
First Author AffilicationCollege of Earth Environmental Sciences
Recommended Citation
GB/T 7714
孙敏淮. 使用机器学习和地表温度数据模拟城市热岛足迹[D]. 兰州. 兰州大学,2020.
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