博碩士論文 110522601 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊工程學系zh_TW
DC.creator施麗雅zh_TW
DC.creatorIsariya Sirivejabandhuen_US
dc.date.accessioned2023-7-14T07:39:07Z
dc.date.available2023-7-14T07:39:07Z
dc.date.issued2023
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/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.descriptionNational Central Universityen_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.abstractAir 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.subjectPM2.5 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.subjectPM2.5 Predictionen_US
DC.subjectData Fusionen_US
DC.subjectGraph-based modelen_US
DC.subjectAir Qualityen_US
DC.subjectDeep Learningen_US
DC.subjectSpatio-Temporal Featureen_US
DC.titleA Graph-based Approach for PM2.5 Predictionen_US
dc.language.isoen_USen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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