請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79750完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張斐章(Fi-John Chang) | |
| dc.contributor.author | Kuan-Yen Wu | en |
| dc.contributor.author | 吳冠諺 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:09:48Z | - |
| dc.date.available | 2021-09-01 | |
| dc.date.available | 2022-11-23T09:09:48Z | - |
| dc.date.copyright | 2021-09-01 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-18 | |
| dc.identifier.citation | 1. ApSimon, H., Oxley, T., Woodward, H., Mehlig, D., Dore, A., Holland, M. (2021). The UK Integrated Assessment Model for source apportionment and air pollution policy applications to PM2.5. Environment International, 153, 106515. 2. Apparicio, P., Gelb, J., Carrier, M., Mathieu, M. È., Kingham, S. (2018). Exposure to noise and air pollution by mode of transportation during rush hours in Montreal. Journal of Transport Geography, 70, 182-192. 3. Abirami, S., Chitra, P. (2021). Regional air quality forecasting using spatiotemporal deep learning. Journal of Cleaner Production, 283, 125341. 4. Brauer, M., Casadei, B., Harrington, R. A., Kovacs, R., Sliwa, K., WHF Air Pollution Expert Group. (2021). Taking a Stand Against Air Pollution—The Impact on Cardiovascular Disease: A Joint Opinion from the World Heart Federation, American College of Cardiology, American Heart Association, and the European Society of Cardiology. Circulation, 143(14), e800-e804. 5. Bai, Y., Li, Y., Wang, X., Xie, J., Li, C. (2016). Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmospheric pollution research, 7(3), 557-566. 6. Cao, Q., Shen, L., Chen, S. C., Pui, D. Y. (2018). WRF modeling of PM2.5 remediation by SALSCS and its clean air flow over Beijing terrain. Science of the Total Environment, 626, 134-146. 7. Chang, F. J., Chen, P. A., Lu, Y. R., Huang, E., Chang, K. Y. (2014). Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. Journal of Hydrology, 517, 836-846. 8. Chang, F. J., Chang, L. C., Kang, C. C., Wang, Y. S., Huang, A. (2020). Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Science of the Total Environment, 736, 139656. 9. Chang, Y. S., Chiao, H. T., Abimannan, S., Huang, Y. P., Tsai, Y. T., Lin, K. M. (2020). An LSTM-based aggregated model for air pollution forecasting. Atmospheric Pollution Research, 11(8), 1451-1463. 10. Deshmukh, P., Kimbrough, S., Krabbe, S., Logan, R., Isakov, V., Baldauf, R. (2020). Identifying air pollution source impacts in urban communities using mobile monitoring. Science of The Total Environment, 715, 136979. 11. Devi, K. I., Meivel, S., Kumar, K. R., Vijayamenaka, J. (2021). A survey report of air polluting data through cloud IoT sensors. Materials Today: Proceedings. 12. Dincer, N. G., Akkuş, Ö. (2018). A new fuzzy time series model based on robust clustering for forecasting of air pollution. Ecological Informatics, 43, 157-164. 13. Feng, R., Zheng, H. J., Gao, H., Zhang, A. R., Huang, C., Zhang, J. X., ... Fan, J. R. (2019). Recurrent Neural Network and random forest for analysis and accurate forecast of atmospheric pollutants: a case study in Hangzhou, China. Journal of cleaner production, 231, 1005-1015. 14. Feng, R., Gao, H., Luo, K., Fan, J. R. (2020). Analysis and accurate prediction of ambient PM2.5 in China using Multi-layer Perceptron. Atmospheric Environment, 232, 117534. 15. Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J. (2015). Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, 118-128. 16. Gan, C. M., Hogrefe, C., Mathur, R., Pleim, J., Xing, J., Wong, D., ... Wei, C. (2016). Assessment of the effects of horizontal grid resolution on long-term air quality trends using coupled WRF-CMAQ simulations. Atmospheric Environment, 132, 207-216. 17. Huang, Z., Yu, Q., Ma, W., Chen, L. (2019). Surveillance efficiency evaluation of air quality monitoring networks for air pollution episodes in industrial parks: Pollution detection and source identification. Atmospheric Environment, 215, 116874. 18. Hu, W., Zhao, T., Bai, Y., Kong, S., Xiong, J., Sun, X., ... Lu, H. (2021). Importance of regional PM2.5 transport and precipitation washout in heavy air pollution in the Twain-Hu Basin over Central China: Observational analysis and WRF-Chem simulation. Science of the Total Environment, 758, 143710. 19. Hepsiba, D., Varalakshmi, L. M., Kumar, M. S., Subburaj, J., Vijayakumar, P., Rani, S. S. (2021). Automatic pollution sensing and control for vehicles using IoT technology. Materials Today: Proceedings, 45, 3019-3021. 20. Haehnel, P., Mareček, J., Monteil, J., O'Donncha, F. (2020). Using deep learning to extend the range of air pollution monitoring and forecasting. Journal of Computational Physics, 408, 109278. 21. Jamil, M. S., Jamil, M. A., Mazhar, A., Ikram, A., Ahmed, A., Munawar, U. (2015). Smart environment monitoring system by employing wireless sensor networks on vehicles for pollution free smart cities. Procedia Engineering, 107, 480-484. 22. Kaivonen, S., Ngai, E. C. H. (2020). Real-time air pollution monitoring with sensors on city bus. Digital Communications and Networks, 6(1), 23-30. 23. Kalia, P., Ansari, M. A. (2020). IOT based air quality and particulate matter concentration monitoring system. Materials Today: Proceedings, 32, 468-475. 24. Khomenko, S., Cirach, M., Pereira-Barboza, E., Mueller, N., Barrera-Gómez, J., Rojas-Rueda, D., ... Nieuwenhuijsen, M. (2021). Premature mortality due to air pollution in European cities: A health impact assessment. The Lancet Planetary Health, 5(3), e121-e134. 25. Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S. (2017, December). Self-normalizing neural networks. In Proceedings of the 31st international conference on neural information processing systems (pp. 972-981). 26. Kumar, R., Kumar, P., Kumar, Y. (2020). Time series data prediction using iot and machine learning technique. Procedia Computer Science, 167, 373-381. 27. Kao, I. F., Liou, J. Y., Lee, M. H., Chang, F. J. (2021). Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts. Journal of Hydrology, 126371. 28. Kao, I. F., Zhou, Y., Chang, L. C., Chang, F. J. (2020). Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting. Journal of Hydrology, 583, 124631. 29. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105. 30. Kumar, D. (2018). Evolving Differential evolution method with random forest for prediction of Air Pollution. Procedia computer science, 132, 824-833. 31. Kow, P. Y., Wang, Y. S., Zhou, Y., Kao, I. F., Issermann, M., Chang, L. C., Chang, F. J. (2020). Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting. Journal of Cleaner Production, 261, 121285. 32. Liu, C., Zhang, S., Gao, Y., Wang, Y., Sheng, L., Gao, H., Fung, J. C. H. (2021). Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model. Science of The Total Environment, 773, 145580. 33. Liu, S., Hua, S., Wang, K., Qiu, P., Liu, H., Wu, B., ... Tian, H. (2018). Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF/Chem simulations and potential source contribution analysis. Science of the total environment, 624, 396-406. 34. Lee, C. C., Tran, M. V., Choo, C. W., Tan, C. P., Chiew, Y. S. (2020). Evaluation of air quality in Sunway City, Selangor, Malaysia from a mobile monitoring campaign using air pollution micro-sensors. Environmental Pollution, 265, 115058. 35. Lin, Y. B., Lin, Y. W., Hsiao, C. Y., Wang, S. Y. (2017). Location-based IoT applications on campus: The IoTtalk approach. Pervasive and mobile computing, 40, 660-673. 36. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26. 37. Liu, H., Yin, S., Chen, C., Duan, Z. (2020). Data multi-scale decomposition strategies for air pollution forecasting: a comprehensive review. Journal of Cleaner Production, 124023. 38. Loomis, D., Grosse, Y., Lauby-Secretan, B., El Ghissassi, F., Bouvard, V., Benbrahim-Tallaa, L., ... Straif, K. (2013). The carcinogenicity of outdoor air pollution. Lancet Oncology, 14(13), 1262. 39. Mihăiţă, A. S., Dupont, L., Chery, O., Camargo, M., Cai, C. (2019). Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling. Journal of Cleaner Production, 221, 398-418. 40. Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., ... Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?. Environment international, 116, 286-299. 41. Masood, A., Ahmad, K. (2020). A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches. Procedia Computer Science, 167, 2101-2110. 42. Nieto, P. G., Lasheras, F. S., García-Gonzalo, E., de Cos Juez, F. J. (2018). PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study. Science of the Total Environment, 621, 753-761. 43. Pimpinella, A., Redondi, A. E., Cesana, M. (2019). Walk this way! An IoT-based urban routing system for smart cities. Computer Networks, 162, 106857. 44. Prasad, K., Gorai, A. K., Goyal, P. (2016). Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmospheric environment, 128, 246-262. 45. Qi, Y., Li, Q., Karimian, H., Liu, D. (2019). A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment, 664, 1-10. 46. Requia, W. J., Roig, H. L., Schwartz, J. D. (2021). Schools exposure to air pollution sources in Brazil: A nationwide assessment of more than 180 thousand schools. Science of The Total Environment, 763, 143027. 47. Shahbazi, H., Karimi, S., Hosseini, V., Yazgi, D., Torbatian, S. (2018). A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models. Atmospheric Environment, 187, 24-33. 48. SM, S. N., Yasa, P. R., Narayana, M. V., Khadirnaikar, S., Rani, P. (2019). Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots. Sustainable Cities and Society, 44, 520-535. 49. Saravanan, D., Kumar, K. S. (2021). Improving air pollution detection accuracy and quality monitoring based on bidirectional RNN and the Internet of Things. Materials Today: Proceedings. 50. Sharma, N., Sharma, R., Jindal, N. (2021). Machine Learning and Deep Learning Applications-A Vision. Global Transitions Proceedings, 2(1), 24-28. 51. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489. 52. Singh, J., Dhiman, G. (2021). A survey on machine-learning approaches: Theory and their concepts. Materials Today: Proceedings. 53. Samal, K. K. R., Babu, K. S., Das, S. K. (2021). Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach. Urban Climate, 36, 100800. 54. Tsai, D. H., Wang, J. L., Chuang, K. J., Chan, C. C. (2010). Traffic-related air pollution and cardiovascular mortality in central Taiwan. Science of the Total Environment, 408(8), 1818-1823. 55. Tran, P. T., Zhao, M., Yamamoto, K., Minet, L., Nguyen, T., Balasubramanian, R. (2020). Cyclists’ personal exposure to traffic-related air pollution and its influence on bikeability. Transportation Research Part D: Transport and Environment, 88, 102563. 56. World Health Organization. (2016). Ambient air pollution: A global assessment of exposure and burden of disease. 57. Wu, C. H., Tsai, I. C., Tsai, P. C., Tung, Y. S. (2019). Large–scale seasonal control of air quality in Taiwan. Atmospheric Environment, 214, 116868. 58. Weichenthal, S., Van Ryswyk, K., Goldstein, A., Shekarrizfard, M., Hatzopoulou, M. (2016). Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model. Environmental pollution, 208, 241-248. 59. Zhang, Z., Wang, J., Kwong, J. C., Burnett, R. T., van Donkelaar, A., Hystad, P., ... Chen, H. (2021). Long-term exposure to air pollution and mortality in a prospective cohort: The Ontario Health Study. Environment International, 154, 106570. 60. Zhou, Y., Chang, L. C., Chang, F. J. (2020). Explore a Multivariate Bayesian Uncertainty Processor driven by artificial neural networks for probabilistic PM2.5 forecasting. Science of the Total Environment, 711, 134792. 61. Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., Wang, Y. S. (2019). Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. Journal of cleaner production, 209, 134-145. 62. Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., Wang, Y. S., Kang, C. C. (2019 A). Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting. Science of The Total Environment, 651, 230-240. 63. Zhang, K., Thé, J., Xie, G., Yu, H. (2020). Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: A case study of Huaihai Economic Zone. Journal of Cleaner Production, 277, 123231. 64. 高雄市政府環境保護局，107年高雄市空氣品質統計年報 65. 張斐章、張麗秋，2015，類神經網路導論原理與應用，滄海書局 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79750 | - |
| dc.description.abstract | 空氣污染為當今人們所關注的議題之一，空氣污染物包含了PM2.5、PM10、O3、SOX、NOX等，其中，以PM2.5對人體造成的危害性最大，環保署於前幾年陸續在工業區附近廣泛設置微型感測器，而臺灣PM2.5預測模式大多為預測環保署測站PM2.5濃度，並藉由空間內插等方式，以輸出區域性的預測結果，然而工業區附近空污的濃度通常較高並且變化大，使得大多數預測模式對於此區域的預測結果仍然有進步之空間，故本研究建立一推估模式(AE-CNN-BP)，由數種ANN模式所組成，包含了自編碼器(AE)、卷積神經網路(CNN)、倒傳遞神經網路(BPNN)，並用於推估T+X時刻高雄市的PM2.5濃度。推估模式基於前人時間序列預測模式於環保署測站預測T+X時刻PM2.5濃度的結果，作為CNN-BP模式的輸入值，以預測編碼(Code)，並將此預測結果作為解碼器(Decoder)的輸入值，即可推估環保署與微型感測器測站T+X時刻之PM2.5濃度值。本研究結果顯示，AE模式在訓練及測試階段之準確度都相當高，RMSE介於8~9.2(μg/m3)之間，R2介於0.93~0.94之間，在預測編碼上，總共建置了三種預測模式，分別為倒傳遞神經網路(BPNN)、深層神經網路(DNN)、混合卷積神經網路(CNN-BP)，結果顯示，CNN-BP模式在預測編碼上獲得最高的準確度；而AE-CNN-BP推估模式應用於高雄市的工業區，結果顯示，在高污染時段下，其PM2.5濃度推估結果較前人研究更為準確，並且在中、高污染時段，更能發揮空污預警之效用，故本研究證明了微型感測器應用於空污預測和預警上是具有價值的。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:09:48Z (GMT). No. of bitstreams: 1 U0001-1808202115134400.pdf: 5325481 bytes, checksum: 0475591ca8b0da309257e50e0757ad07 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 謝誌 i 中文摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xi 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 章節架構 3 第二章 文獻回顧 4 2.1 空氣污染對於人體的健康危害 4 2.2 微型感測器應用於空污相關文獻 5 2.3 深度學習相關文獻 6 第三章 理論概述 8 3.1 類神經網路 8 3.2 類神經網路優化 9 3.3 倒傳遞神經網路 9 3.4 深層神經網路 11 3.5 卷積神經網路 12 3.6 自編碼器 14 3.7 防止過擬合技術 - Early Stopping 16 3.8 活化函數 17 3-9 克利金法 19 第四章 研究案例 20 4.1 研究區域 20 4-2 研究資料蒐集 23 4-3 研究架構及流程 27 4-4 模式評估指標 31 第五章 結果與討論 32 5.1 Autoencoder結果 32 5.1.1 AE模式使用的資料 32 5.1.2 AE模式Code數量的決定 32 5.1.3 AE網路層數及神經元數 33 5.1.4 AE參數設置 33 5.1.5 AE模式訓練測試效果 34 5-2 預測Code模式結果 37 5-2-1 模式使用的資料 37 5-2-2 Code統計值 38 5-2-3 BPNN結果 39 5-2-4 DNN結果 43 5-2-5 CNN-BP結果 47 5-3 最佳預測Code模式之比較及其校正圖結果 53 5-4 空污污染時段的選取 57 5-5 克利金結果 59 5-6 研究限制 73 第六章 結論 74 6.1 結論 74 文獻回顧 77 附錄一 BPNN預測微型感測器 83 附錄二 高雄市克利金與真實值誤差結果(01/06 01:00:00) 84 附錄三 高雄市三個行政區克利金與真實值誤差結果(01/06 01:00:00) 85 附錄四 高雄市克利金與真實值誤差結果(10/30 19:00:00) 87 附錄五 高雄市三個行政區克利金與真實值誤差結果(10/30 19:00:00) 88 附錄六 微型感測器監測項目及偵測範圍 90 | |
| dc.language.iso | zh-TW | |
| dc.subject | 臺灣 | zh_TW |
| dc.subject | PM2.5預測 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 微型感測器 | zh_TW |
| dc.subject | 自編碼器 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 倒傳遞神經網路 | zh_TW |
| dc.subject | PM2.5 forecast | en |
| dc.subject | Autoencoder | en |
| dc.subject | Micro-sensor | en |
| dc.subject | Deep learning | en |
| dc.subject | Taiwan | en |
| dc.subject | Back Propagation Neural Network (BPNN) | en |
| dc.subject | Convolutional Neural Network (CNN) | en |
| dc.title | 建立大數據深度學習模式以推估區域性多時刻空氣品質-以高雄市為案例 | zh_TW |
| dc.title | Building a deep learning model with big-data to estimate regional multi-time air quality - a case study of Kaohsiung City | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 潘述元(Shu-Yuan Pan) | |
| dc.contributor.oralexamcommittee | 張麗秋(Hsin-Tsai Liu),黃文政(Chih-Yang Tseng),周崇光 | |
| dc.subject.keyword | PM2.5預測,深度學習,微型感測器,自編碼器,卷積神經網路,倒傳遞神經網路,臺灣, | zh_TW |
| dc.subject.keyword | PM2.5 forecast,Deep learning,Micro-sensor,Autoencoder,Convolutional Neural Network (CNN),Back Propagation Neural Network (BPNN),Taiwan, | en |
| dc.relation.page | 90 | |
| dc.identifier.doi | 10.6342/NTU202102471 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-19 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1808202115134400.pdf | 5.2 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。