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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張斐章 | |
dc.contributor.author | Che-Chia Kang | en |
dc.contributor.author | 康哲嘉 | zh_TW |
dc.date.accessioned | 2021-06-17T08:06:05Z | - |
dc.date.available | 2019-08-20 | |
dc.date.copyright | 2019-08-20 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-20 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73562 | - |
dc.description.abstract | 近年來,細懸浮微粒物(PM2.5)為許多已開發國家空氣污染的監測指標,定義為懸浮於空氣中微小的固體顆粒或液滴。由於粒徑小能夠深入至人體肺部,當空氣中含有高濃度的PM2.5會對人體健康造成威脅,因此能夠掌握PM2.5的濃度變化特性至關重要。自2012年以來,PM2.5在台灣被立法定為空氣污染物。然而,PM2.5的生成機制非常複雜,包括自然和人為來源。當地環境資源和交通運輸混雜而成的多重關係,使得掌控且準確預測PM2.5成為一項非常具有挑戰性的議題。
本研究以台灣北部地區作為案例,探討PM2.5的空間和時間特徵有助於判別其可能的污染源。自組特徵映射類神經網路(SOM)可以對高維數據集進行分類,形成有意義的拓撲結構,具有信息提取及可視化的優點。考量到聚類特性和視覺化效果,SOM能夠有效探索高維度多變量空氣污染的相互關係。接著透過分析不同類型站點高污染事件造成的污染原因及來源,比較時空關係的變化,找出與PM2.5濃度變化相關的因素。最後藉由機器學習方法建立模型預測PM2.5濃度。主要結果顯示:(1)人口密度高及交通運輸流量大的地區會造成PM2.5濃度的變化大; (2)PM2.5的濃度變化明顯受到季節遷移的影響; (3)發生長時間大範圍的空氣污染的原因,主要係由於境外霾害所影響;(4)Gamma Test非線性方法的結果是解析PM2.5生成機制的一種方法;(5)不同類型測站受高汙染事件影響程度不同,造成預測PM2.5濃度的準確度有相當大的差異。 | zh_TW |
dc.description.abstract | In recent years, fine particulate matter (PM2.5) has been a critical air pollutant in many developed countries. Exposure to high concentrations of PM2.5 can cause serious health problems because PM2.5 contains microscopic solid or liquid droplets that are sufficiently small to ingest deep into human lungs. Thus, a precise daily prediction of PM2.5 concentration is notably important to regulatory plans that inform the public and social activities when injury events are foreseen. PM2.5 was legislated as an air pollutant in Taiwan since 2012. However, the generation of PM2.5 is very complicated, including natural and artificial sources. The mix of local sources and regional transportations makes the control and accurate prediction of PM2.5 a very challenge work. Analyzing the spatial and temporal characteristics of PM2.5 could help identifying its possible emission sources. The self-organizing map (SOM) can classify high-dimensional datasets to form a meaningful topological map and has advantages of information extraction and visualization. Bearing in mind the clustering capability and visual interpretation, the SOM is used to explore the interrelationships of high-dimensional multivariate air pollution systems. Analyze the causes and sources of pollution caused by high-level pollution events at different types of stations, compare the changes in time-space relationship, find out the factors related to PM2.5 concentration changes. Finally, establish a model by machine learning method to predict PM2.5 concentration, where the air quality datasets under different time and space in the northern region of Taiwan are used as study case. The main results shows: (1) high density of population and traffic area usually bring high change of PM2.5 concentration. (2) The change of season bring obviously effect. (3) The cause of long-term and large-scale air pollution is mostly due to the damage caused by overseas disasters. (4) The Gamma test method is an effective way to analyze the PM2.5 generation mechanism. (5) The PM2.5 concentration at various monitoring stations are affected by different levels of extreme air pollution events that significantly influences the accuracy of predicting PM2.5 concentration obtained by the machine learning method. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:06:05Z (GMT). No. of bitstreams: 1 ntu-108-R06622024-1.pdf: 6058229 bytes, checksum: eec1c39f27dfee4ca205230c7934fa26 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 謝誌 I
摘要 IV Abstract VI 圖目錄 XI 表目錄 XIII 第一章 前言 1 1.1 研究緣起 1 1.2 研究目的 2 第二章 文獻回顧 3 2.1 空氣污染及懸浮微粒對健康影響相關文獻 3 2.2 機器學習方法用於空氣污染相關研究 4 2.3 自組特徵映射類神經網路相關研究 6 2.4 克利金法 8 2.5 Gamma Test非線性方法相關研究 9 第三章 理論概述 10 3.1 自組特徵映射網路 10 3.1.1 自組特徵映射網路架構 10 3.1.2 自組特徵映射網路演算法 11 3.2 克利金法 15 3.2.1 克利金系統方程式 15 3.2.2 半變異數 18 3.2.3 理論半變異數 19 3.3 Gamma Test非線性方法 20 3.4 倒傳遞類神經網路 22 第四章 研究案例 24 4.1 研究區域 24 4.2 資料蒐集 26 4.3 研究流程架構 27 4.4 評估指標 30 第五章 結果與討論 32 5.1 區域PM2.5分類模式SOM 32 5.2 高污染濃度事件分析 42 5.3 空氣污染因子分析 54 5.4 預測模式建立 61 第六章 結論與建議 74 6.1 結論 74 6.2 建議 75 第七章 參考文獻 77 附錄一 Gamma Test 篩選結果 85 附錄二 模式一之完整預測結果 87 附錄三 模式二之完整預測結果 89 附錄四 模式三之完整預測結果 91 | |
dc.language.iso | zh-TW | |
dc.title | 機器學習探索PM2.5濃度變化的時空關係 | zh_TW |
dc.title | Machine learning to explore the temporal and spatial relationship of PM2.5 concentration changes | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張麗秋,黃文政,李奇旺,王怡心 | |
dc.subject.keyword | 自組特徵映射類神經網路(SOM),細懸浮微粒(PM2.5),Gamma test方法,倒傳遞類神經網路(BPNN), | zh_TW |
dc.subject.keyword | Self-Organizing Neural Networks (SOM),PM2.5,Gamma test method,Back Propagation Neural Network (BPNN), | en |
dc.relation.page | 92 | |
dc.identifier.doi | 10.6342/NTU201904057 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-20 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
顯示於系所單位: | 生物環境系統工程學系 |
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