| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 施麗雅 | zh_TW |
| DC.creator | Isariya Sirivejabandhu | en_US |
| dc.date.accessioned | 2023-7-14T07:39:07Z | |
| dc.date.available | 2023-7-14T07:39:07Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=110522601 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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. | en_US |
| DC.subject | PM2.5 預測 | zh_TW |
| DC.subject | 數據融合 | zh_TW |
| DC.subject | 圖模型 | zh_TW |
| DC.subject | 空氣品質 | zh_TW |
| DC.subject | 深度學習 | zh_TW |
| DC.subject | 空間時間特徵 | zh_TW |
| DC.subject | PM2.5 Prediction | en_US |
| DC.subject | Data Fusion | en_US |
| DC.subject | Graph-based model | en_US |
| DC.subject | Air Quality | en_US |
| DC.subject | Deep Learning | en_US |
| DC.subject | Spatio-Temporal Feature | en_US |
| DC.title | A Graph-based Approach for PM2.5 Prediction | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |