博碩士論文 etd-0805118-084145 詳細資訊


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姓名 林俞均(Yu-Chun Lin) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 106學年第2學期
論文名稱(中) 應用自適應神經模糊推理技術預測空氣品質指數
論文名稱(英) Employing Adaptive Neuro-Fuzzy Inference Technique for Prediction of Air Quality Indices
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    紙本論文:5 年後公開 (2023-09-05 公開)

    電子論文:使用者自訂權限:校內 5 年後、校外 5 年後公開

    論文語文/頁數 中文/50
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    摘要(中) 本論文擬提出一個基於模糊類神經網路的空氣品質預測系統,透過歷史的資訊進行訓練,資料以時間序列的型式輸入,便能依照時間的變化來預測空氣品質與環境因子的未來走勢。時間序列預測被廣泛地應用在很多領域,例如金融股市的預測、電力品質的預測等等,其有效性也被證實。由於影響因子的不確定性,我們在預測系統裡加入模糊的元素。我們的預測系統是一個四層的模糊類神經網路,分別是輸入層、模糊層、推理層和輸出層。首先,我們對訓練資料做分群得到模糊群聚,每個群聚的歸屬函數由學習到的平均值和變異值所決定;得到模糊群聚後利用遺傳算法與粒子群優化算法的結合對模糊規則的參數進行優化;系統的輸出是經由模糊推論所導出的。我們提出的方法有下列的優點:(1)加入模糊元素,可以更適當的考慮空氣汙染影響因子的不確定性;(2)模糊群聚契合訓練資料的分布,並且由量化的平均值和變異值所描述;(3)模糊規則是由訓練資料萃取出來的,免除使用者的不便;(4)透過參數優化使模糊規則品質提升;(5) 當新的訓練資料加入時,模糊規則不需要重新產生,有利於系統的更新與維護。
    摘要(英) This paper proposes an air quality prediction system based on neuro-fuzzy neural networks which can be trained through historical record information.
    Time series training data are employed to forecast the air quality and environmental factors in the future. Due to the uncertainty of the involved impact factors, fuzzy elements are added to the forecasting system. Our prediction system is a four-layer fuzzy neural network, consisting of the input layer, fuzzy layer, inference layer, and output layer. First, training data are partitioned into fuzzy clusters whose membership functions are of characterized by the learned means and variances. From these fuzzy clusters. Next, genetic and particle swarm optimization algorithms are applied to refine the parameters of the fuzzy rules. The output of the system, indicating the forecast air quality indices, is derived through the fuzzy inference process.
    Our proposed approach has the following advantages: (1) Adding fuzzy elements can more appropriately deal with the uncertainty of the air pollution impact factors; (2) The distribution of training data can be described more precisely by fuzzy clusters with quantified means and variances; (3) Fuzzy rules are extracted automatically from the training data, instead of being supplied manually by human experts; (4) The obtained fuzzy rules are of high quality, and the associated parameters can be optimized efficiently; (5) When new training data are acquired, the fuzzy rules do not need to be regenerated, which is beneficial to the system update and maintenance.
    關鍵字(中)
  • 空氣品質
  • 模糊規則
  • 類神經網路
  • 時間序列預測
  • 參數優化。
  • 關鍵字(英)
  • Air quality
  • Fuzzy rules
  • Neural network
  • Time series
  • Optimization algorithms.
  • 論文目次 論文審定書......i
    誌謝......ii
    摘要......iii
    Abstract......iv
    圖目錄......vii
    表目錄......viii
    第一章 導論......1
       1.1. 研究背景與目的......1
       1.2. 問題描述......2
       1.3. 論文架構......3
    第二章 文獻探討......5
       2.1. 自建構分群(Self-Constructing Clustering, SCC-I)......5
       2.2. 神經模糊系統模型(A Neuro-Fuzzy System Modeling)......6
    第三章 研究方法......7
       3.1. 利用Pearson相關係數計算出變數間的相關性......8
       3.2. 建立訓練樣本......9
       3.3. 透過迭代自建構分群SCC-I進行分群......12
       3.4. 建立Fuzzy rules......14
       3.5. 利用混合行算法優化Fuzzy rules參數......16
       3.6. 預測出空氣品質未來值與計算AQI空氣品質指標......18
    第四章實驗結果......21
       4.1. Pearson相關係數實驗......22
       4.2. PCA測試......23
       4.3. 更新參數的影響......24
       4.4. 與實際政府預測AQI比較......25
       4.5. 台灣台中大里區數據實驗結果......25
       4.6. RMCAB實驗結果......28
       4.6.1. 與ANN-MLP模型比較RMCAB 2015年日平均值......28
       4.6.2. 與ANN-MLP模型比較RMCAB資料比較小時預測結果......31
    第五章 結論與未來展望 34
       4.7. 結論......34
       4.8. 未來研究方向......34
    參考文獻......35
    參考文獻 [1] M. C. Turner, D. Krewski, C. A. Pope III, Y. Chen, S. M. Gapstur, and M. J. Thun, “Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers,” American journal of respiratory and critical care medicine, vol. 184, no. 12, pp. 1374–1381, 2011.
    [2] J. Tian and D. Chen, “A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements,” Remote Sensing of Environment, vol. 114, no. 2, pp. 221–229, 2010.
    [3] B. Ostro, L. Chestnut, N. Vichit-Vadakan, and A. Laixuthai,“The impact of particulate matter on daily mortality in Bangkok, Thailand,” Journal of the Air & Waste Management Association, vol. 49, no. 9, pp. 100–107, 1999.
    [4] D. W. Dockery and C. A. Pope, “Acute respiratory effects of particulate air pollution,” Annual Review of Public Health, vol. 15, no. 1, pp. 107–132, 1994.
    [5] K. Katsouyanni, G. Touloumi, C. Spix, J. Schwartz, F. Balducci, S. Medina, G. Rossi, B. Wojtyniak, J. Sunyer, L. Bacharova et al., “Short term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project,” British Medical Journal, vol. 314, no. 7095, p. 1658, 1997.
    [6] D. W. Dockery, C. A. Pope, X. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris Jr, and F. E. Speizer, “An association between air pollution and mortality in six US cities,” New England Journal of Medicine, vol. 329, no. 24, pp. 1753– 1759, 1993.
    [7] C. A. Pope, M. J. Thun, M. M. Namboodiri, D. W. Dockery, J. S. Evans, F. E. Speizer, C. W. Heath et al., “Particulate air pollution as a predictor of mortality in a prospective study of US adults,” American Journal of Respiratory and Critical Care Medicine, vol. 151, no. 3, pp. 669–674, 1995.
    [8] C. A. Pope III, R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito, and G. D. Thurston, “Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution,” Journal of the American Medical Association, vol. 287, no. 9, pp. 1132–1141, 2002.
    [9] C. Monn, “Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone,” Atmospheric Environment, vol. 35, no. 1, pp. 1–32, 2001.
    [10] M. Amodio, E. Andriani, G. de Gennaro, A. D. Loiotile, A. Di Gilio, and M. Placentino, “An integrated approach to identify the origin of PM10 exceedances,” Environmental Science and Pollution Research, vol. 19, no. 8, pp. 3132–3141, 2012.
    [11] S. Rodrıguez, X. Querol, A. Alastuey, G. Kallos, and O. Kakaliagou, “Saharan dust contributions to PM10 and TSP levels in southern and eastern Spain,” Atmospheric Environment, vol. 35, no. 14, pp. 2433–2447, 2001.
    [12] C. Monn, O. Braendli, G. Schaeppi, C. Schindler, U. Ackermann-Liebrich, P. Leuenberger, S. Team et al., “Particulate matter< 10μm (PM10) and total suspended particulates (TSP) in urban, rural and alpine air in Switzerland,” Atmospheric Environment, vol. 29, no. 19, pp. 2565–2573, 1995.
    [13] C.-Y. L. Lin-Ong Zhang, Mon-Ling Chiang, “Factors affecting suspended particulate matter (PM10) - a case study of traffic air quality monitoring stations in taiwan,” Journal of Soil and Water Conservation, vol. 47, no. 1, pp. 1235–1246, 2015.
    [14] N. Leksmono, J. Longhurst, K. Ling, T. J. Chatterton, B. Fisher, and J. Irwin, “Assessment of the relationship between industrial and traffic sources contributing to air quality objective exceedences: a theoretical modelling exercise,” Environmental Modelling & Software, vol. 21, no. 4, pp. 494–500, 2006.
    [15] V. Mallet and B. Sportisse, “Air quality modeling: From deterministic to stochastic approaches,” Computers & Mathematics with Applications, vol. 55, no. 10, pp. 2329–2337, 2008.
    [16] H. J. Fernando, M. Mammarella, G. Grandoni, P. Fedele, R. Di Marco, R. Dimitrova, and P. Hyde, “Forecasting PM10 in metropolitan areas: Efficacy of neural networks,” Environmental Pollution, vol. 163, pp. 62–67, 2012.
    [17] W. G. Cobourn and M. C. Hubbard, “An enhanced ozone forecasting model using air mass trajectory analysis,” Atmospheric Environment, vol. 33, no. 28, pp. 4663–4674, 1999.
    [18] W. G. Cobourn, “Accuracy and reliability of an automated air quality forecast system for ozone in seven Kentucky metropolitan areas,” Atmospheric Environment, vol. 41, no. 28, pp. 5863–5875, 2007.
    [19] Cobourn, W. Geoffrey. "An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations." Atmospheric Environment vol. 44, no. 25, pp. 3015-3023, 2010
    [20] Y. Lin and W. G. Cobourn, “Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions,” Atmospheric Environment, vol. 41, no. 16, pp. 3502–3513, 2007
    [21] J. L. Pearce, J. Beringer, N. Nicholls, R. J. Hyndman, and N. J. Tapper, “Quantifying the influence of local meteorology on air quality using generalized additive models,” Atmospheric Environment, vol. 45, no. 6, pp. 1328–1336, 2011.
    [22] P. Perez and J. Reyes, “An integrated neural network model for PM10 forecasting,” Atmospheric Environment, vol. 40, no. 16, pp. 2845–2851, 2006.
    [23] D. Voukantsis, K. Karatzas, J. Kukkonen, T. Räsänen, A. Karppinen, and M. Kolehmainen, “Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki,” Science of the Total Environment, vol. 409, no. 7, pp. 1266–1276, 2011.
    [24] H. Abderrahim, M. R. Chellali, and A. Hamou, “Forecasting PM10 in algiers: efficacy of multilayer perceptron networks,” Environmental Science and Pollution Research, vol. 23, no. 2, pp. 1634–1641, 2016.
    [25] J. Kukkonen, L. Partanen, A. Karppinen, J. Ruuskanen, H. Junninen, M. Kolehmainen, H. Niska, S. Dorling, T. Chatterton, R. Foxall et al., “Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki,” Atmospheric Environment, vol. 37, no. 32, pp. 4539–4550, 2003.
    [26] J. Hooyberghs, C. Mensink, G. Dumont, F. Fierens, and O. Brasseur, “A neural network forecast for daily average PM10 concentrations in Belgium,” Atmospheric Environment, vol. 39, no. 18, pp. 3279–3289, 2005.
    [27] P. Perez and J. Reyes, “Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile,” Atmospheric Environment, vol. 36, no. 28, pp. 4555–4561, 2002.
    [28] G. Corani, “Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning,” Ecological Modelling, vol. 185, no. 2-4, pp. 513–529, 2005.
    [29] G. Grivas and A. Chaloulakou, “Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece,” Atmospheric Environment, vol. 40, no. 7, pp. 1216–1229, 2006.
    [30] J. Ordieres, E. Vergara, R. Capuz, and R. Salazar, “Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua),” Environmental Modelling & Software, vol. 20, no. 5, pp. 547–559, 2005.
    [31] S.-J. Lee and C.-S. Ouyang, “A neuro-fuzzy system modeling with self-constructing rule generationand hybrid svd-based learning,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 3, pp. 341–353, 2003.
    [32] Li, Lianfa, J. Wu, N. Hudda, C. Sioutas, S. A. Fruin and R. J. Delfino, "Modeling the concentrations of on-road air pollutants in southern California." Environmental Science and Technology,” vol. 47, no. 16, pp. 9291-9299, 2013.
    [33] RMCAB, “Bogotá air quality monitoring network. website of vironmental information,” http://201.245.192.252:81/, 2015.
    [34] M.W. Gardner and S.R. Dorling, “Artificial neural networks (the multilayer perceptron) – A review of applications in the atmospheric sciences,” Atmospheric environment vol. 32, no. 32., pp. 2627-2636, 1998.
    [35] S. Haykin, “Neural Networks – A Comprehensive Foundation,” College Publishing Company, New York, 1999.
    [36] W.G. Cobourn, L. Dolcine, M. French and M.C. Hubbard, “A comparison of nonlinear regression and neural network models for ground-level ozone forecasting,” Journal of the Air & Waste Management Association, vol. 50, no. 11, pp.1999-2009, 2000.
    [37] J. Hooyberghs, C. Mensink, G. Dumont, F. Fierens and O. Brasseur, “A neural network forecast for daily average PM10 concentrations in Belgium,” Atmospheric Environment, vol. 39, no. 18, pp. 3279-3289, 2005.
    [38] M. W. Matt, S. R. Dorling, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,” Atmospheric Environment, vol. 32, no. 14-15, pp. 2627-2636, 1998.
    [39] D. K. Papanastasiou, D. Melas and I. Kioutsioukis, “ Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium–sized Mediterranean city,” Water Air and Soil Pollution, vol. 182,no. 1-4, pp. 325-334, 2007.
    [40] A. Russo, P. G. Lind, F. Raischel, R. Trigo and M. Mendes, “Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales,” Atmospheric Pollution Research, vol. 6, no. 3, pp. 540-549, 2015.
    [41] “Taiwan environmental protection administration, executive yuan air quality data website,” https://taqm.epa.gov.tw/taqm/tw/YearlyDataDownload.aspx, 2018.
    [42] L. Chen and T.-Y. Pai, “Comparisons of GM(1,1), and BPNN for predicting hourly particulate matter in Dali area of Taichung city, Taiwan,” Atmospheric Pollution Research, vol. 6, no. 4, pp. 572 – 580, 2015.
    [43] RMCAB, “Bogotá air quality monitoring network. website of vironmental information,” http://201.245.192.252:81/, 2015.
    [44] F. Franceschi, M. Cobo, and M. Figueredo, “Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering,” Atmospheric Pollution Research, 2018.
    口試委員
  • 吳志宏 - 召集委員
  • 侯俊良 - 委員
  • 劉志峰 - 委員
  • 歐陽振森 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2018-07-26 繳交日期 2018-09-05

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