博碩士論文 etd-0919121-030338 詳細資訊


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姓名 毛郁舜(YU-SHUN MAO) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Department of Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 109學年第2學期
論文名稱(中) 多通道時間混合神經網路應用於空氣品質預測
論文名稱(英) Effective Approaches to Air Quality Prediction Using Multi-Channel Temporal Hybrid Neural Network
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    紙本論文:5 年後公開 (2026-10-19 公開)

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    論文語文/頁數 英文/71
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    摘要(中) 隨著城市化和工業化的嚴重,空氣品質逐漸下降。都市圈是受到空氣污染影響的最大的族群。隨著城市化和工業化的發展,空氣污染已經成為一個嚴重的環境問題。空氣品質預測被認為是重要的議題,早期預警和控制可以減少對都市居民的健康影響。本論文提出多通道時間混合神經網絡來預測空氣品質。該模型包括擴張卷積層、雙向閘循環單元層和注意力層,分別提取空間特徵、時間特徵和關鍵特徵。該混合了學習數據的不同維度,即時間維度和特徵維度。這結構該結構能夠建立時間特徵和特徵關聯,同時實驗證實此結構能夠準確預測空氣品質。我們在實驗中採用了來自(台灣 左營)、(中國 北京)、(哥倫比亞 甘迺迪)和(哥倫比亞 波利瓦爾公園)的數據集。實驗證明,我們提出的模型在大多數情況下都是贏過傳統的回歸方法、神經網絡和其他作者提出的方法。
    摘要(英) In recent years increasingly serious air pollution, the air quality decline gradually. Especially people living in the city circle is affected by air pollution the most ethnic groups. With the quick advancement of urbanization and industrialization, air pollution has become a serious issue in developing countries. Air quality prediction is considered as a major issue, and early warning and control can reduce the health impact on the residents in the area. This paper aims to predict air quality by multi-channel temporal hybrid neural network. This model includes dilated convolution layer, bidirectional gate recurrent unit layer and attention layer which extract spatial features, temporal features and key features respectively. The hybrid structure learns different dimensions of data, namely the temporal dimension and the feature dimension. This allows the structure to build temporal dependencies and feature associations to learn higher-level features. The proposed model takes into account the first 168 hours of different pollutant data, meteorological data from a single station. We employ real world dataset from (Zuoying, Taiwan), (Beijing, China), (Kennedy, Colombia) and (Simon Bolivar Park, Colombia) in experiment. The results show that our proposed model is superior to the traditional regression method, neural network and other methods in most cases. It also shows that our proposed model can handle air quality predictions with high accuracy for next 24 hours ahead prediction.
    關鍵字(中)
  • 卷積神經網路
  • 雙向閘循環單元
  • 注意力層
  • 混合結構
  • 空氣品質預測
  • 關鍵字(英)
  • Convolutional Neural Network(CNN)
  • Bidirectional Gate Recurrent Unit(Bi-GRU)
  • Attention Layer
  • Hybrid Structure
  • Air Quality Prediction
  • 論文目次 Contents
    Verification Letter i
    Acknowledgement ii
    Chinese Abstract iii
    Abstract iv
    List of Figures vii
    List of Tables ix
    Chapter 1 Introduction 1
    Chapter 2 Related Work 4
    2.1. Feature Selection 4
    2.2. The Methods for Air Quality Prediction 4
    2.3. Impact of Meteorological Conditions on Air Pollution 5
    Chapter 3 Proposed Method 6
    3.1. Normalization 7
    3.2. Selection of Input Variables 8
    3.3. Building Training Examples 9
    3.4. Temporal Hybrid Neural Network 11
    3.4.1. One Dimensional Dilated Convolution Layer 12
    3.4.2. Bidirectional Gated Recurrent Unit Layer 14
    3.4.3. Attention Layer 16
    3.4.4. Dense Layer 17
    Chapter 4 Experiments 19
    4.1. Introduction to Datasets 19
    4.2. Data Exploration 21
    4.2.1. Missing Values 22
    4.2.2. Data Distribution 23
    4.3. Details of Experiments 25
    4.3.1. Machine Learning Approach 26
    4.3.2. Deep Learning Approach 26
    4.4. Experiment Results 29
    4.4.1. The result of feature selection 31
    4.4.2. Comparison of Single Step Ahead Prediction Performance 33
    4.4.3. Comparison of Multi-Step Ahead Prediction Performance 35
    4.5. Statistical Analysis for Experiment Results 44
    4.6. Discussion 45
    4.6.1. The analysis of window size and structure 46
    4.6.2. The Analysis of Machine Learning Method and Deep Learning Method 48
    Chapter 5 Conclusion 53
    Reference 54
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