A Graph-based Approach for PM2.5 Prediction

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

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姓名

施麗雅(Isariya Sirivejabandhu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(A Graph-based Approach for PM2.5 Prediction)

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至系統瀏覽論文 (2024-7-7以後開放)

摘要(中)

空氣污染是一個對人類健康和環境造成重大風險的全球問題。 PM2.5 是呼吸道和心血管疾病的主要因素。準確的 PM2.5 預測對於了解污染模式、保護公眾健康、環境規劃和政策制定至關重要。在這項研究中，我們進行了多次實驗，以改進 PM2.5 的預測能力，並開發了一個全面的 PM2.5 預測系統，其中包括 AirBox 數據預處理、EPA 數據預處理、數據融合、特徵工程、特徵選擇和提出的預測模型 DCRNN-GS。我們提出的模型專為迭代多步驟的PM2.5預測而設計，使用過去 24 小時的數據來預測未來 24 小時的情況。結果顯示，我們提出的系統在 PM2.5 預測方面優於最先進的方法。

摘要(英)

Air pollution is a global issue with significant risks to human health and the environment. PM2.5 is a major contributor to respiratory and cardiovascular diseases. Accurate PM2.5 prediction is crucial for understanding pollution patterns, protecting public health, environmental planning, and policy development. In this research, we conducted several experiments to improve PM2.5 prediction and developed a comprehensive PM2.5 prediction system that includes AirBox data preprocessing, EPA data preprocessing, data fusion, feature engineering, feature selection, and the proposed prediction model, DCRNN-GS. Our proposed model is specifically designed for iterative multi-step PM2.5 prediction, using the data from the past 24 hours to predict the next 24 hours. The results show that our proposed system outperforms the state-of-the-art approaches in PM2.5 prediction.

關鍵字(中)

★ PM2.5 預測
★ 數據融合
★ 圖模型
★ 空氣品質
★ 深度學習
★ 空間時間特徵

關鍵字(英)

★ PM2.5 Prediction
★ Data Fusion
★ Graph-based model
★ Air Quality
★ Deep Learning
★ Spatio-Temporal Feature

論文目次

Contents
1 Introduction 1
2 Related Work 4
2.1 Machine Learning PM2.5 Prediction . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Deep Learning PM2.5 Prediction . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.1 Feedforward Neural Network based . . . . . . . . . . . . . . . . . . 5
2.2.2 Recurrent Neural Network (RNN) based . . . . . . . . . . . . . . . 6
2.2.3 Convolutional Neural Networks (CNN) based . . . . . . . . . . . . 7
2.2.4 Graph Neural Networks (GNN) based . . . . . . . . . . . . . . . . . 7
2.3 Hybrid Model PM2.5 Prediction . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Preliminary 10
3.1 HyperImpute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 DeepKriging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 GraphSAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.4 Diffusion Convolutional Recurrent Neural Network . . . . . . . . . . . . . 13
3.4.1 Bidirectional Random Walk . . . . . . . . . . . . . . . . . . . . . . 14
3.4.2 Diffusion Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.4.3 Diffusion Convolutional Layer . . . . . . . . . . . . . . . . . . . . . 15
3.4.4 Diffusion Convolutional Gated Recurrent Unit . . . . . . . . . . . . 16
3.4.5 Scheduled Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4.6 Sequence to Sequence Architecture . . . . . . . . . . . . . . . . . . 16
4 Design 18
4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3 Research Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.4 Proposed System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4.1 EPA Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . 23
4.4.2 AirBox Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . 25
4.4.3 Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.4.4 Feature Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4.5 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4.6 Diffusion Convolutional Recurrent Neural Network with Graph-
SAGE (DCRNN-GS) . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 Performance 35
5.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.4 Experimental Results and Analysis . . . . . . . . . . . . . . . . . . . . . . 40
5.4.1 Data Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.4.2 Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.4.3 Window Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.4.4 PM2.5 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.5 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.5.1 Model Combination . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.5.2 Activation-Optimization Combination . . . . . . . . . . . . . . . . . 45
5.5.3 Impact of Additional Features on Model Accuracy . . . . . . . . . . 46
6 Conclusion 48

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指導教授

孫敏德(Min-Te Sun)

審核日期

2023-7-14

推文