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研究生:蔡欣樺
研究生(外文):TSAI, HSIN-HUA
論文名稱:以深度學習模型探討亞熱帶都市熱島垂直結構熱特性
論文名稱(外文):The Characteristic of the Vertical Structure of Urban Heat Island by Deep Learning
指導教授:黃志弘黃志弘引用關係
指導教授(外文):HUANG, CHIH-HONG
口試委員:吳可久林鑑澄張效通杜功仁
口試委員(外文):WU, KO-CHIULIN, JIAN-CHENGCHANG, HSIAO-TUNGTU, KUNG-JEN
口試日期:2020-05-25
學位類別:博士
校院名稱:國立臺北科技大學
系所名稱:設計學院設計博士班
學門:設計學門
學類:綜合設計學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:123
中文關鍵詞:都市熱島熱島垂直結構深度學習都市氣候因子都市邊界層都市冠層
外文關鍵詞:Urban Heat IslandVertical structure of heat islandDeep learningUrban climate factorsUrban boundary layerUrban canopy
ORCID或ResearchGate:0000-0002-6930-8789
相關次數:
  • 被引用被引用:5
  • 點閱點閱:341
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  • 下載下載:36
  • 收藏至我的研究室書目清單書目收藏:0
都市土地使用及景觀規劃影響都市熱環境的分佈,在都市熱島效應的相關研究上,多為近地層的平面觀察,以都市氣象學角度切入,都市熱島效應具有明顯的空間特性,在熱島的疏導上不應只在平面上敘述,應包含都市的冠層及邊界層,研究透過不同尺度與不同氣象因子下的數據,探討都市熱環境的垂直結構。
研究以結構方程模式,整合氣象水文研究資料庫之數據,來了解影響都市熱環境之因子;並透過平面與垂直熱島結構的實地量測,進行都市熱島地表面氣象因子與景觀元素的剖析,得出以濕度、風速結論之影響主因子,二者作為探空氣球數據進行深度學習演算歸納之參數指標;透過深度學習結果了解垂直結構熱分佈之樣態與地表舒適度之關聯,進而探討都市熱島邊界層與冠層的消長與特性。
研究結果證實存在邊界層與冠層現象,且邊界層逆溫現象落在77m-667m之間,顯示邊界層有消長的現象,且熱島邊界層結構在冬季時較厚;透過板橋單點垂直結構樣態分析,推論台北都會區的熱島邊界層應為一層厚薄不一的棉被,而非一般圖示都市熱島圖的半圓形穹頂。根據深度學習演算出的樣態,都會區0至6000m區間的氣溫垂直遞減率應由理論數值每上升100m溫度下降0.649℃,修正為本研究所運算的夏季板橋下降0.54℃與花蓮下降0.53℃,冬季修正為板橋下降0.43℃與花蓮下降0.42℃;且樣態斜率呈現冬季溫度偏離理論值斜率的現象比夏季更大,顯示冬季的都市熱島現象更為顯著。透過深度學習與大數據分析,未來可作為探討都市規劃之手法,讓垂直熱流可更加速上升並且作為降溫移除熱量對策之參考依據。

Urban land use and landscape planning influence the distribution of heat in the urban environment. Most of the existing researches on the urban heat island effect adopted the near-surface plane and the perspective of urban meteorology. The urban heat island effect should have clear spatial characteristics, and the transfer of heat should not be discussed on planes alone; it should include the urban canopy and boundary layer. Relevant research should investigate the vertical structure of the urban heat environment using different scales and weather factors.
This study compiled data from the Data Bank for Atmospheric & Hydrologic Research and employed structural equation modeling to examine the factors of the urban heat environment. Using actual ground surface and vertical structure measurements, we analyzed the weather factors and landscape elements on the ground that influence urban heat islands. With the concluding factors humidity and wind speed as the foundation and as sounding balloon data, we used deep learning to determine the parameter indexes and understand the relationship between the patterns of heat distributions in vertical structures and thermal comfort on the ground surface. In this way, we examined the variation and characteristic in the boundary layer and canopy of urban heat islands.
Our results demonstrate that boundary layer and canopy phenomena indeed exist and that the temperature inversion phenomenon was present between 77 m and 667 m of the boundary layer, thereby demonstrating that variation exist in the boundary layer. Furthermore, the boundary layer structure of the heat island is thicker in the winter. By performing vertical structure pattern analysis at a single location in Banqiao, we infer that the boundary layer of the heat island in the Taipei Metropolitan Area is a blanket of uneven thickness rather than the semicircular dome generally shown in urban heat island diagrams. Based on the pattern by deep learning algorithm, the lapse rate of temperature between 0 m and 6000 m should be revised from the theoretical value of a 0.649℃ decline for every increase of 100 m in elevation to a decrease of 0.54℃ in Banqiao and a decrease of 0.53℃ in Hualien in the summer and a decrease of 0.43℃ in Banqiao and a decrease of 0.42℃ in Hualien in the winter. In addition, the pattern slopes show greater deviations in winter temperatures from the theoretical value slope than in summer temperatures, which indicates that the urban heat island effect is more significant in the winter. Deep learning and big data analysis can be applied to examine urban planning in the future so as to accelerate rises in vertical heat flows and provide reference for cooling and heat removal measures.

摘要 i
ABSTRACT iii
誌謝 v
目錄 vii
圖目錄 xiii
表目錄 xvii
第一章 緒論 1
1.1 研究緣起 1
1.2 研究目的 2
1.3 研究範圍及內容 3
1.4 研究方法 3
1.5 研究限制 4
1.6 名詞解釋 5
1.7 研究流程 6
第二章 文獻回顧 7
2.1 都市熱環境相關理論 7
2.1.1 都市氣候尺度 7
2.1.2 都市熱島效應 8
2.1.3 都市邊界層與都市冠層 9
2.1.4 氣象情境差異 10
2.1.5 都市熱舒適度 11
2.2 都市氣候理論 12
2.2.1 都市熱平衡 12
2.2.2 太陽熱輻射 13
2.2.3 環境風場對流 14
2.2.4 環境蒸發散 14
2.2.5 都市結構分類 15
2.3 都市物理環境觀測與量測技術 16
2.3.1 傳統與非傳統氣象觀測 16
2.3.2 都市物理環境量測技術 16
2.4 深度學習相關理論與應用 17
2.4.1 深度學習基礎架構 17
2.4.2 機器學習、統計與深度學習的區別 17
2.4.3 深度學習現況應用 18
第三章 研究操作方法 21
3.1 結構方程模式 22
3.1.1 氣象資料補缺 22
3.1.2 氣象資料校正 22
3.1.3 研究資料結構轉換 23
3.2 實際量測法 24
3.2.1 都市微氣候量測 25
3.2.2 數據參數轉換 25
3.2.3 圖面可視化分析 29
3.3 CFD數值模型模擬 29
3.3.1 模型建置 29
3.3.2 環境參數設置 30
3.4 深度學習演算 30
3.4.1 氣象數據結構化 31
3.4.2 資料庫建立 31
3.4.3 氣象數據整理 32
3.4.4 氣象數據演算 32
3.4.5 數據圖表繪製 32
第四章 都市型態與都市景觀規劃元素能量分佈與舒適度探討 33
4.1 結構方程模式探討氣象因子對都市熱環境舒適度分析 34
4.1.1 氣象數據搜集與背景 34
4.1.2 研究模式建構與分析結果 36
4.1.3 都市熱環境與舒適指標分析 39
4.1.4 環境熱焓與熱舒適度範圍分析 42
4.1.5 結果討論 46
4.2 都市環境熱焓分佈之探討 46
4.2.1 環境實測內容與背景 46
4.2.2 土地使用比對量測參數與熱焓比較分析 47
4.2.3 空氣線圖舒適區 50
4.2.4 結果討論 51
4.3 敷地景觀規劃元素蓄熱分佈分析 52
4.3.1 環境實測內容與背景 52
4.3.2 都市計畫空間元素影響環境物理參數 53
4.3.3 模型數值條件與驗證分析 55
4.3.4 結果討論 59
4.4 都市景觀地表特徵垂直結構的熱特性之觀測 59
4.4.1 環境實測內容與背景 59
4.4.2 不同地表材質環境與垂直結構資料剖析 61
4.4.3 綜合分析 64
4.4.4 結果討論 65
4.5 綜合討論 66
第五章 深度學習演算法評估都市氣候因子影響對垂直結構之樣態 67
5.1 都市垂直結構數位資料庫建置 67
5.1.1 探空氣象資料搜集 67
5.1.2 數據格式整理 67
5.1.3 NDVI都市土地使用結構分類 68
5.2 深度學習演算法評估 69
5.2.1 深度學習梯度優化 69
5.2.2 深度學習框架 70
5.3 都市垂直結構樣態與舒適度探討 72
5.3.1 不同季節與時段於垂直結構深度學習演算樣態 72
5.3.2 不同都市型態垂直溫度與熱焓演算樣態 72
5.3.3 舒適度與垂直結構樣態之關聯 74
5.3.4 影響舒適度的氣象因子 76
5.4 無法深度學習演算之樣態 79
5.5 不同變因下的邊界層與冠層探討 80
5.5.1 逆溫數據取得 80
5.5.2 都會區熱島邊界層探討 81
5.5.3 都會區熱島冠層探討 84
5.5.4 邊界層與冠層高度消長變因 87
5.6 都市垂直結構差異相關性討論 90
第六章 結論與建議 93
6.1 結論 93
6.2 建議 96
6.2.1 都市景觀規劃元素應用設計參考策略 96
6.2.2 後續研究建議 98
參考文獻 99
附錄 114
A垂直逆溫數據圖 114
B 邊界層平均高度數值表 121
C 冠層平均高度數值表 123
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研討會論文
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109.Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., & Aram, F. (2020). State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability. Paper presented at the Engineering for Sustainable Future, Cham.
110.Su, B., & Duan, G. (2010). Research of the solar photovoltaic cells output characteristics influenced by infrared wave in the solar spectrum. Paper presented at the Proceedings of SPIE - The International Society for Optical Engineering.
111.Suberk, N. T., & Ates, H. F. (2019). Deep Learning for Building Density Estimation in Remotely Sensed Imagery. Paper presented at the UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering.
112.Tsai ,H.-H., Huang, C.-H. (2018). Surveying the Thermal Properties of an Urban Heat Island Vertical Structure Influenced by Subtropical Solar Radiation. Paper presented at the International Conference on Environment and Natural Science (ICENS), Taipei,Taiwan.
113.Yoshida, S. (2006). Development of Three Dimensional Plant Canopy Model for Numerical Simulation of Outdoor Thermal Environment. the 6th International Conference on Urban Climate (ICUC 6), Goteborg, Sweden, June 12-16, 2006.
114.Yu, D., Seide, P., & Li, G. (2012). Conversational speech transcription using context-dependent deep neural networks. Paper presented at the Proceedings of the 29th International Conference on Machine Learning, ICML 2012.

學位論文
115.杜映儒,2018,以熱容觀點探討都市規劃元素對於敷地環境蓄熱之關聯性,碩士論文,國立臺北科技大學。
116.張慕恩,2011,以遙感探測探討台北市綠覆率與空氣溫度之關係,碩士論文,中國文化大學
117.簡佑倫,2015,亞熱帶地區以建築設計被動式控制微氣候空氣動力與熱焓之策略,碩士論文,國立臺北科技大學。

其他
118.Khan, A., Ahmad, S., & Baig, M. H. A. (2016). Application of TCT as a Remote Sensing Change Detection Technique: A Temporal Case Study of Lahore District – Pakistan.
119.Mohn, E. (2019). SQL (Structured Query Language). Salem Press Encyclopedia of Science.
120.Oke, T., & Canada. (2006). Initial guidance to obtain representative meteorological observations at urban sites.
121.Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D., Deering, W. (1973) Monitoring vegetation systems in the Great Plains with ERTS, ERTS Third Symposium, NASA SP-351 I, pp. 309-317.
122.Дмитро Тереник, & Георгій Кучук Анатолійович. (2020). Sql & Nosql Database Comparison by Case Designing Affiliate System Report. Радіоелектронні і Комп’ютерні Системи, 1, 83.
123.美國冷凍空調學會(ASHRAE)

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