以訓練模型為基礎之空氣污染物預測分析研究

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姓名

陳姿羽(Tzu-Yu Chen) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

以訓練模型為基礎之空氣污染物預測分析研究
(The Study of the Air Pollutants Concentration Analysis and Prediction Based on Model Training)

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至系統瀏覽論文 (2026-9-1以後開放)

摘要(中)

現今社會隨著工業化和都市化加速的同時，污染物經由汽車排放、工業生產和能源消耗等過程釋放至大氣中，其造成空氣污染已成為一個嚴重的全球性問題，而參與AQI評價的污染物不僅造成空氣方面的污染，對人體健康影響更如同是無形的毒藥。
透過機器學習模型之運用，建立一個準確預測空氣污染物濃度的模型與訓練，以期能夠對未來的空氣品質進行預測分析。模型訓練上搭配線性回歸分析來探討空氣污染物的相關性，並採用梯度下降法作為資料收斂的最優化演算法，以最小化目標函數及達成模型之預測。
以不同數據資訊之組合進行預測分析訓練，另考慮了各地區對於氣候及污染物間的相關性與可預測性之可能差異，故亦分別依照地區選擇不同的測站作分析驗證比較。本論文以空氣中二氧化硫濃度作為預測值，來探究氣候條件與空氣污染物之間的相關性。選擇了適當的特徵值進行預測分析，探究了模型系統預測值與特徵值之間的相關性與可預測性確認，最後建立一個有效之預測模型，達到最佳之預測成效。

摘要(英)

Nowadays, due to the acceleration of industrialization and urbanization, pollutants are released into the atmosphere through several activities such as vehicle emission, industrial production, live wastes disposition and energy consumption. Air pollution has become a serious global problem. The pollutants involved in the AQI evaluation cause air pollution, also act like the invisible poisons to human health.
By model training with machine learning, a model for accurately predicting the concentration of air pollutants was established to analyze the air quality and predict the future status. The model training was combined with the linear regression analysis to explore the correlation and prediction model of air pollutants. The gradient descent method was used as the optimization algorithm in for data convergence in order to minimize the objective function in this study.
The model training combines the linear regression analysis to explore the correlation between the air pollutants and uses the gradient descent as the optimization algorithm for data convergence to minimize the objective function and achieve the prediction of the model. Prediction analysis and training was conducted using different combinations of data and information. In addition, considering the differences of correlation and predictability may exist between the climate and pollutants in various regions, in the study, select ed different measuring stations according regions as the basis for comparison and verification. This study selected the sulfur dioxide in the air as the prediction value to explore the correlation between the climate conditions and the air pollutants. Selected the appropriate feature values to conduct the prediction analysis, explored the correlation and the predictability between the prediction values of the model system and the feature values. Finally, established an effective prediction model and achieved the best prediction results for the air quality.

關鍵字(中)

★ 訓練模型
★ 多元線性回歸
★ 梯度下降法
★ 預測分析

關鍵字(英)

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章緒論 1
1.1. 研究背景 1
1.2. 研究動機與目的 1
1.3. 章節概要 2
第二章相關研究背景 3
2.1. 空氣品質指標（AQI）介紹 3
2.2. 機器學習介紹 3
2.3. 梯度下降法介紹 4
2.4. 相關文獻 5
第三章研究方法 10
3.1. 整體流程 10
3.2. 訓練組合 11
3.3. 訓練過程 13
3.4. 測站地區 18
第四章研究結果與討論 19
4.1. 實驗測試環境 19
4.2. 研究工具 20
4.3. 模型訓練 21
4.4. 訓練資料分析 22
4.5. 訓練結果 28
4.6. 測試結果 32
4.6.1. 單一特徵值預測分析 33
4.6.2. 相依性最高兩項特徵值預測分析 38
4.6.3. 氣候特徵值預測分析 41
4.6.4. 污染物特徵值預測分析 45
4.6.5. 所有特徵值預測分析 49
第五章結論 70
參考文獻 72

參考文獻

[1] K. Liu et al., “Ambient air pollution and Children’s health: An umbrella review,” Atmospheric Pollution Research, pp. 102108–102108, Mar. 2024, doi: https://doi.org/10.1016/j.apr.2024.102108.
[2] ‌J. Fu, H. Fu, C. Zhu, Y. Sun, and H. Cao, “Assessing the health risk impacts of urban green spaces on air pollution - Evidence from 31 China’s provinces,” Ecological Indicators, vol. 159, p. 111725, Feb. 2024, doi: https://doi.org/10.1016/j.ecolind.2024.111725.
[3] ‌Y. Qin, C. Sun, D. Li, H. Zhang, H. Wang, and Y. Duan, “Does urban air pollution have an impact on public health? Empirical evidence from 288 prefecture-level cities in China,” Urban Climate, vol. 51, p. 101660, Sep. 2023, doi: https://doi.org/10.1016/j.uclim.2023.101660.
[4] ‌G. N. Ike, O. C. Obieri, and O. Usman, “Modelling the air pollution induced health effects of energy consumption across varied spaces in OECD countries: An asymmetric analysis,” Journal of environmental management, vol. 349, pp. 119550–119550, Jan. 2024, doi: https://doi.org/10.1016/j.jenvman.2023.119550.
[5] ‌L. Holtgrewe et al., “Clearing the Air: Assessing Healthcare Professionals’ Awareness of Air Pollution’s Health Consequences,” Future healthcare journal, pp. 100130–100130, Apr. 2024, doi: https://doi.org/10.1016/j.fhj.2024.100130.
[6] C.-Y. Wu, C.-Y. Wu, M.-C. Li, H. J. Ho, and C.-K. Ao, “Association of air quality index (AQI) with incidence of atopic dermatitis in Taiwan: A nationwide population-based cohort study,” Journal of the American Academy of Dermatology, Feb. 2024, doi: https://doi.org/10.1016/j.jaad.2024.01.058.
[7] ‌J. Zhang et al., “Independent and interaction effects of prenatal exposure to high AQI and extreme Humidex on the risk of preterm birth: A large sample population study in northern China,” Reproductive Toxicology (Elmsford, N.Y.), vol. 124, p. 108544, Mar. 2024, doi: https://doi.org/10.1016/j.reprotox.2024.108544.
[8] M. Ikram and Zhi Jun Yan, “Statistical Analysis of the Impact of AQI on Respiratory Disease in Beijing: Application Case 2009,” Energy procedia, vol. 107, pp. 340–344, Feb. 2017, doi: https://doi.org/10.1016/j.egypro.2016.12.169.
[9] 思愛普SAP公司網站:
https://www.sap.com/taiwan/products/artificial-intelligence/what-is-machine-learning.html
[10] CSDN 梯度下降（Gradient Descent）小結:
https://blog.csdn.net/qq_31747765/article/details/108710803
[11] P. T. Hoai, N. T. Vinh, and N. P. H. Chung, “A novel stepsize for gradient descent method,” Operations Research Letters, vol. 53, p. 107072, Mar. 2024, doi: https://doi.org/10.1016/j.orl.2024.107072.
[12] ‌ S. S. Du, X. Zhai, B. Poczos, and A. Singh, “Gradient Descent Provably Optimizes Over-parameterized Neural Networks,” arXiv.org, Feb. 04, 2019. https://arxiv.org/abs/1810.02054 (accessed Apr. 25, 2024).
[13] ‌ J. Tang, “Fractional gradient descent algorithm for switching models using self-organizing maps: One set data or all the collected data,” Chaos, Solitons & Fractals, vol. 172, p. 113460, Jul. 2023, doi: https://doi.org/10.1016/j.chaos.2023.113460.
[14] 大大通人工智慧-梯度下降法:
https://www.wpgdadatong.com/blog/detail/40537
[15] T. Peng et al., “Research and application of a novel selective stacking ensemble model based on error compensation and parameter optimization for AQI prediction,” Environmental research, vol. 247, pp. 118176–118176, Apr. 2024, doi: https://doi.org/10.1016/j.envres.2024.118176.
[16] M. A. A. Al-qaness, H. Fan, A. A. Ewees, D. Yousri, and M. Abd Elaziz, “Improved ANFIS model for forecasting Wuhan City Air Quality and analysis COVID-19 lockdown impacts on air quality,” Environmental Research, vol. 194, p. 110607, Mar. 2021, doi: https://doi.org/10.1016/j.envres.2020.110607.
[17] Seyedeh Reyhaneh Shams et al., “Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forcasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran),” Environmental Pollution, vol. 338, pp. 122623–122623, Dec. 2023, doi: https://doi.org/10.1016/j.envpol.2023.122623.
[18] Amazon Web Services, AWS:
https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/splitting-the-data-into-training-and-evaluation-data.html
[19] Pythonic Finance:Cost functions, gradient descent, and gradient boost
https://randlow.github.io/posts/machine-learning/cost-func-gradient-descent-boost/
[20] Medium : Optimizers in Deep Learning
https://musstafa0804.medium.com/optimizers-in-deep-learning-7bf81fed78a0
[21] 環境部環境資料開放平臺
https://data.moenv.gov.tw/
[22] X. He, S. Pang, J. Ma, and Y. Zhang, “Influence of relative humidity on heterogeneous reactions of O 3 and O 3 /SO 2 with soot particles: Potential for environmental and health effects,” Atmospheric Environment, vol. 165, pp. 198–206, Sep. 2017, doi: https://doi.org/10.1016/j.atmosenv.2017.06.049.
[23] 交通部中央氣象署
https://www.cwa.gov.tw/V8/C/C/Statistics/monthlydata.html
[24] 中央氣象署有發佈警報的颱風列表
https://zh.wikipedia.org/zh-tw/%E4%B8%AD%E5%A4%AE%E6%B0%A3%E8%B1%A1%E7%BD%B2%E6%9C%89%E7%99%BC%E4%BD%88%E8%AD%A6%E5%A0%B1%E7%9A%84%E9%A2%B1%E9%A2%A8%E5%88%97%E8%A1%A8
[25] 台灣整合防災工程技術顧問公司總監-賈新興臉書
https://www.facebook.com/photo.php?fbid=900200214807263&set=pb.100044518775906.-2207520000&type=3
[26] 環境部空氣品質監測網
https://airtw.moenv.gov.tw/cht/Encyclopedia/pedia09/pedia9_2.aspx
[27] 機電工程署
https://www.emsd.gov.hk/energyland/tc/energy/environment/pollutants.html
[28] 交通部公路局統計查詢網
https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100&funid=11100
[29] 經濟部營運中工廠家數
https://dmz26.moea.gov.tw/GA/common/Common.aspx?code=G&no=2
[30] 國家環境毒物研究中心
https://nehrc.nhri.edu.tw/2021/03/26/%E4%B8%80%E6%B0%A7%E5%8C%96%E7%A2%B3/

指導教授

陳彥文

審核日期

2024-7-1

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