政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/149030

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/149030

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政大機構典藏 > 資訊學院 > 社群網路與人智計算國際研究生博士學位學程(TIGP) > 學位論文 > Item 140.119/149030

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题名:	使用衛星數據監測台灣空氣汙染物遠程輸送，優化PM2.5預測 Using Satellite Data on Remote Transportation of Air Pollutants for PM2.5 Prediction in Taiwan
作者:	喬治·威廉·基比里奇 Kibirige, George william
贡献者:	陳孟彰劉昭麟 Meng Chang Chen Chao-Lin Liu 喬治·威廉·基比里奇 George william Kibirige
关键词:	PM2.5 空氣污染臺灣 PM2.5 Air Pollution Composite Neural Network Taiwan
日期:	2023
上传时间:	2024-01-02 15:22:16 (UTC+8)
摘要:	精確的PM2.5預測是對抗空氣污染的重要一環，有助於政府制定環境政策。透過多角度大氣校正（MAIAC）算法處理的衛星遙感氣膠光學深度（AOD）資料，我們能夠觀察遙遠地區之間污染物的傳輸情況。在本論文中，我們提出了一種結合神經網絡模型的方法，稱為遠程傳輸污染（RTP）模型，可更準確預測當地的PM2.5濃度。本論文所提出的RTP模型結合了多個深度學習元件，並從異質特徵中學習。此外，我們還提出的分類算法，來檢測從AOD數據中識別出的兩個參考站點的遠程傳輸污染事件（RTPEs）。我們在台灣的北部地區和南部中部地區進行了大量模擬，以評估其在實際數據上的表現。對於北部地區，提出的RTP模型在+4小時至+24小時、+28小時至+48小時和+52小時至+72小時的時間範圍內，相對不考慮RTPEs的基本模型，提高了17%至30%、23%至26%和18%至22%的準確度，同時也優於只考慮RTPEs的最先進模型，提高了12%至22%、12%至14%和10%至11%的準確度其他影響PM2.5濃度的特徵，陸海風在台灣南部和中部地區扮演著關鍵角色。我們在提出的RTP模型中使用了大面積海洋風特徵，以捕捉陸海風對PM2.5的影響。我們對RTP模型的不同組成特徵個別進行研究，結果顯示不管再提出特徵跟模型改進，我們提出的架構在整體性能方面都有明顯改進。我們還進行了月份分析，證明了在台灣南部和中部地區，陸海風經常發生並主導PM2.5濃度。 Accurate PM2.5 prediction is part of the fight against air pollution that helps governments to manage environmental policy. Satellite Remote sensing aerosol optical depth (AOD) pro- cessed by The Multi-Angle Implementation of Atmospheric Correlation (MAIAC) algorithm allows us to observe the transportation of remote pollutants between regions. In this dissertation we propose a composite neural network model, the Remote Trans- ported Pollutants (RTP) model, for such long-range pollutant transportation that predicts more accurate local PM2.5 concentrations using such satellite data. The proposed RTP model integrates several deep learning components and learns from the heterogeneous features of various domains. We also detected remote transportation pollution events (RTPEs) at two reference sites from the AOD data using proposed classification algorithm. Extensive experi- ments using real-world data were conducted in two regions, northern region and southern and central region of Taiwan. For northern region the proposed RTP model outperforms the base model that does not account for RTPEs by 17%-30%, 23%-26% and 18%-22% and state-of-the-art models that account for RTPEs by 12%-22%, 12%-14%, and 10%-11% at +4h to +24h, +28h to +48 hours, and +52h to +72h hours respectively. Including other features that affect PM2.5, Land-sea breeze plays an important role in increasing the level of PM2.5 in southern and central region of Taiwan. We used extra feature of large interpolation ocean wind in our proposed RTP model to capture the impact of land-sea breeze. We investigated the model outputs of different components of the RTP and concluded that the proposed composite neural network architecture yields significant improvements in the overall performance compared to each component and the other state-of-the-art model. We performed monthly analysis which also demonstrated the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central region of Taiwan in the months when land-sea breeze dominates the accumulation of PM2.5.
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描述:	博士國立政治大學社群網路與人智計算國際研究生博士學位學程(TIGP) 104761506
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0104761506
数据类型:	thesis
显示于类别:	[社群網路與人智計算國際研究生博士學位學程(TIGP)] 學位論文

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