以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：82

、訪客IP：3.137.170.183

姓名	陳瑜(Tran Huynh Duy)  查詢紙本館藏	畢業系所	大氣科學學系
論文名稱	利用深度學習之長短期記憶模型預報台灣地面PM2.5濃度 (Deep Learning–based LSTM Algorithm in Forecasting Surface PM2.5 concentration in Taiwan)
相關論文	★ 鹿林山背景站大氣輻射及氣膠輻射驅動力之研究 ★ 中南半島生質燃燒氣膠濃度分布之年際變化與其對區域環境衝擊研究 ★ 中壢地區光達消光散射比之長期分析與污染物關聯性研究 ★ 臺灣大氣背景PM2.5質量濃度之推估 ★ 雲林斗六PM2.5濃度變化與氣膠光學特性及氣象條件之關聯性研究 ★ Mapping Surface Solar Radiation with Satellite Data over Taiwan ★ 開發適用於大氣邊界層觀測的無人機系統 ★ 利用AERONET資料解析中南半島地區氣膠種類及成分 ★ 氣膠對臺灣北部暖雲微物理和毛雨的影響 ★ Characteristics and Corrections of Thermal Offset for Secondary Standard Pyranometers ★ 氣膠對臺灣中部平原夏季降水日變化之影響 ★ 中南半島生質燃燒氣膠傳送動力機制及區域氣候反饋 ★ 2019年春季泰國北部無人機觀測實驗: 邊界層特徵與氣膠垂直分布之研究 ★ Investigating hygroscopic cloud-seeding effects in liquid-water clouds in northern Taiwan: in-situ measurements and model simulation ★ 整合無人機與光達觀測解析斗六地區空污事件之演變過程 ★ 氣膠光學及微物理反演法開發：以鹿林山大氣背景站應用為例
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-12-31以後開放)
摘要(中)	機器學習為評估空氣品質的強大工具，它可以提供即時及預測資訊，以利公眾採取相關措施減緩空氣品質惡化。本研究使用長短期記憶模型(Long Short-Term Memory, LSTM)來進行短期空氣品質預報，並針對雲林斗六的PM2.5濃度進行探討，斗六地區的空氣品質問題可能是由於複雜的排放源，受當地環流與地形所影響，使斗六成為台灣空氣污染最嚴重的地區之一。因此本研究使用環保署斗六空氣品質觀測站連續監測數據為模型訓練主要資料來源，同時使用AERONET所提供氣膠光學厚度(AOD)、氣象站提供氣壓資料，及鄰近斗六三個環保署空品監測站所提供PM2.5濃度作為輔助提供預測效能，以模式中不同的設定進行模型敏感度測試，並尋找最佳模型組合。 以2021年斗六預報1小時至預報24小時為24個個案進行預報，評估模型敏感度，而各個案的RMSE從6.4至13.1 ug m-3不等，預報1小時至預報24小時的相關性則從0.92逐漸降低至0.58，其中，在預報1小時的PM2.5濃度預報趨勢與實際監測質趨勢相符。另外，本評估模型可預測高污染事件，如PM2.5濃度達100 ug m-3，即本方法可克服其他模式所發生低估的問題。然而，當預報時間拉長，模式仍會出現低估，如當預報24小時，最高可預估到PM2.5濃度僅達50-60 ug m-3。另外，本研究發現使用深度學習的LSTM (Deep learning-based LSTM)，即兩層模型，PM2.5濃度預報將有顯著改善，在加入鄰近三站PM2.5濃度觀測值等輔助資料後，RMSE可改善將近10%。除預報1小時至24小時，本研究也針對季節性及區域性預報結果進行評估。在季節特徵方面，從觀測資料顯示，冬季為斗六地區污染最嚴重的季節，觀測值與預報結果誤差約為16.2 ug m-3，而夏季的觀測值與預報結果誤差呈現最低，為5.5 ug m-3，可能原因為夏季是相對較無污染的季節，PM2.5濃度不高，卻也使相關性較低。在區域特徵方面，本研究以台灣西部沿海地區共十個環保署空品站資料來進行模式評估，並針對預報1小時、預報12小時及預報24小時進行討論。研究發現，中南部站點的趨勢與斗六站最相近，而北部站點表現最差，RMSE較高、相關性較低。 總結來說，本研究考量台灣氣候條件並納入多樣的觀測數據，建立良好人工智慧模型架構，提供空氣品質預報作業有良好的參考，該模型可應用於台灣都市地區空氣品質評估，並提供預警以利公衛相關單位進行應變措施。
摘要(英)	Machine learning has become a powerful tool in air quality assessment which can provide timely and predictable information, alert the public, and take timely measures to prevent deteriorating air quality. The study used an LSTM algorithm to predict short-term air quality. We focused on the PM2.5 concentration in Douliu, one of the most polluted sites in Taiwan. The challenge of Douliu’s air quality issue may be due to the complicated emission source and the effect of local circulation and topography. The EPA air quality data from the Douliu station was used as the primary input for model training. A sensibility test of different model setups was performed to rule out the best combination. The auxiliary features like AOD from the AERONET database, Pressure from CWB open source, time indicators, and PM2.5¬ ¬concentration from three nearby stations were also considered to improve the prediction performance. The 24 cases represent 1- to 24-hour prediction in Douliu, 2021, was conducted to assess the model sensitivity. The optimal setup was selected with the best performance, whose RMSE varied from 6.4 to 13.1 µg/m3 over the 24 cases. The highest correlation was 0.92 for the 1-hour prediction, and the lowest was 0.58 for the next 24-hour forecast. The distribution value of predicted PM2.5 at the 1-hour forecast shows a consistent PM2.5 concentration with the variation of the observed PM2.5. Additionally, the model can predict the high PM2.5 event, nearly 100 µg/m3. This result indicated that the LSTM algorithm could overcome the underestimated issue, which is the practical problem with other algorithms. However, if we considered predicting at the longer prediction time, the model still met the underestimated issue. This could be seen in the 24-hour prediction model, which only predicted the high PM.2.5 event at 50 – 60 µg/m3. Additionally, using the Deep learning-based LSTM (using two layers of LSTM), the PM2.5 concentration from the model prediction shows an improvement. After considering the auxiliary features, the combination with PM2.5 features from the three nearby stations shows better performances, with the improvement in RMSE can reach to nearly 10%. Seasonal and regional testing was conducted to assess the performance of the proposed model. The seasonal variation showed that the highest error, about 16.2 µg/m3, was observed during the winter, which is the high-polluted season in the area. On the other hand, the lowest error, 5.5 µg/m3, was observed during the summer; however, this also resulted in the lowest correlation. Because the summer is not the polluted season, leading to the low PM2.5 concentration. Regarding regional testing, ten stations in the western coastal region of Taiwan were selected to assess the model’s performance. Additionally, the prediction for the next 1-, 12-, and 24-hours models were chosen for comparison. The central and southern Taiwan stations present a similar trend to the Douliu station. On the other hand, the northern Taiwan stations perform the worst with a higher RMSE and lower correlation. Overall, this study provides a good reference for the best settings for deep learning–based AI model which meets Taiwan’s climate conditions and data resources. The model can be implemented for routine air quality monitoring in urban areas and air-quality alarms associated with public health.
關鍵字(中)	★ 深度學習 ★ LSTM ★ PM2.5 ★ 台灣 ★ 斗六	關鍵字(英)	★ Deep-Learning ★ LSTM ★ PM2.5 ★ Taiwan ★ Douliu
論文目次	Table of Contents Abstract I 摘要 III Acknowledgments IV Table of Contents V List of Tables VII List of Figures VIII Chapter 1 – Introduction 1 1.1. Air quality and effects on public health 1 1.2. Modeling in assessing the air quality 6 1.3. Study objectives 10 Chapter 2 – Site and methodology 11 2.1. Site description 12 2.2. Data collections 12 2.2.1. EPA data 13 2.2.2. AERONET AOD data 13 2.2.3. CWB Pressure data 13 2.3. Long short-term memory (LSTM) algorithm 14 2.4. Data pre-processing 16 2.4.1. Features scaling 16 2.4.2. Principal Component Analysis (PCA) 17 2.5. Assessment criteria of the model performance 18 Chapter 3 – Results and discussion 19 3.1. LSTM algorithm setup and optimization process 19 3.1.1. Number of hidden units and learning rate (α) 19 3.1.2. Selecting the number of epochs 21 3.1.3. Weight initialization 23 3.1.4. Selecting the activation function 26 3.1.5. Selecting the feature scaling method 28 3.1.6. Model performance for the PM2.5 prediction 31 3.2. Comparison between 1 and 2 layers of LSTM performance 33 3.3. Model’s input data selection 36 3.3.1. Leave-one-out assessment for EPA data 36 3.3.2. Consideration of adding new features to the input dataset 39 3.4. Evaluation of the performance of the LSTM algorithm 41 3.4.1. LSTM algorithm performance in the prediction 41 3.4.2. Seasonal effect on the LSTM algorithm 44 3.4.3. Regional effect on the LSTM model 46 3.4.4. The comparison between the LSTM algorithm and other deep-learning algorithms 50 Chapter 4 – Conclusion 52 References 54
參考文獻	Abdi, H., & Williams, L. J. (2010). Principal component analysis. In Wiley Interdisciplinary Reviews: Computational Statistics (Vol. 2, Issue 4, pp. 433–459). https://doi.org/10.1002/wics.101 Afzali, A., Rashid, M., Afzali, M., & Younesi, V. (2017). Prediction of air pollutants concentrations from multiple sources using AERMOD coupled with WRF prognostic model. Journal of Cleaner Production, 166, 1216–1225. https://doi.org/10.1016/j.jclepro.2017.07.196 Apte, J. S., Brauer, M., Cohen, A. J., Ezzati, M., & Pope, C. A. (2018). Ambient PM2.5 Reduces Global and Regional Life Expectancy. Environmental Science and Technology Letters, 5(9), 546–551. https://doi.org/10.1021/acs.estlett.8b00360 Bi, C., Chen, Y., Zhao, Z., Li, Q., Zhou, Q., Ye, Z., & Ge, X. (2020). Characteristics, sources and health risks of toxic species (PCDD/Fs, PAHs and heavy metals) in PM2.5 during fall and winter in an industrial area. Chemosphere, 238. https://doi.org/10.1016/j.chemosphere.2019.124620 Cambra-López, M., Aarnink, A. J. A., Zhao, Y., Calvet, S., & Torres, A. G. (2010). Airborne particulate matter from livestock production systems: A review of an air pollution problem. In Environmental Pollution (Vol. 158, Issue 1, pp. 1–17). https://doi.org/10.1016/j.envpol.2009.07.011 Cambra-López, M., Hermosilla, T., Lai, H. T. L., Aarnink, A. J. A., & Ogink, N. W. M. (n.d.). PARTICULATE MATTER EMITTED FROM POULTRY AND PIG HOUSES: SOURCE IDENTIFICATION AND QUANTIFICATION. Transactions of the ASABE, 54(2), 629–642. Cambra-López, M., Torres, A. G., Aarnink, A. J. A., & Ogink, N. W. M. (2011). Source analysis of fine and coarse particulate matter from livestock houses. Atmospheric Environment, 45(3), 694–707. https://doi.org/10.1016/j.atmosenv.2010.10.018 Castell, N., Mantilla, E., Salvador, R., Stein, A. F., & Millán, M. (2010). Photochemical model evaluation of the surface ozone impact of a power plant in a heavily industrialized area of southwestern Spain. Journal of Environmental Management, 91(3), 662–676. https://doi.org/10.1016/j.jenvman.2009.09.030 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. https://doi.org/10.1016/j.scitotenv.2020.139656 Chen, R., Hu, B., Liu, Y., Xu, J., Yang, G., Xu, D., & Chen, C. (2016). Beyond PM2.5: The role of ultrafine particles on adverse health effects of air pollution. Biochimica et Biophysica Acta - General Subjects, 1860(12), 2844–2855. https://doi.org/10.1016/j.bbagen.2016.03.019 Cheng, F. Y., & Hsu, C. H. (2019). Long-term variations in PM2.5 concentrations under changing meteorological conditions in Taiwan. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-43104-x Choi, J., Oh, J. Y., Lee, Y. S., Min, K. H., Hur, G. Y., Lee, S. Y., Kang, K. H., & Shim, J. J. (2018a). Harmful impact of air pollution on severe acute exacerbation of chronic obstructive pulmonary disease: Particulate matter is hazardous. International Journal of COPD, 13, 1053–1059. https://doi.org/10.2147/COPD.S156617 Choi, J., Oh, J. Y., Lee, Y. S., Min, K. H., Hur, G. Y., Lee, S. Y., Kang, K. H., & Shim, J. J. (2018b). Harmful impact of air pollution on severe acute exacerbation of chronic obstructive pulmonary disease: Particulate matter is hazardous. International Journal of COPD, 13, 1053–1059. https://doi.org/10.2147/COPD.S156617 Corani, G., & Scanagatta, M. (2016). Air pollution prediction via multi-label classification. Environmental Modelling and Software, 80, 259–264. https://doi.org/10.1016/j.envsoft.2016.02.030 Doreswamy, Harishkumar, K. S., Km, Y., & Gad, I. (2020). Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models. Procedia Computer Science, 171, 2057–2066. https://doi.org/10.1016/j.procs.2020.04.221 Fang, S.-H., & Chen, H.-W. (1996). Atmospheric Enuironmnt (Vol. 30, Issue 5). Fu, H., Zhang, M., Li, W., Chen, J., Wang, L., Quan, X., & Wang, W. (2012). Morphology, composition and mixing state of individual carbonaceous aerosol in urban Shanghai. Atmospheric Chemistry and Physics, 12(2), 693–707. https://doi.org/10.5194/acp-12-693-2012 Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 Hsu, C. H., & Cheng, F. Y. (2016). Classification of weather patterns to study the influence of meteorological characteristics on PM2.5 concentrations in Yunlin County, Taiwan. Atmospheric Environment, 144, 397–408. https://doi.org/10.1016/j.atmosenv.2016.09.001 Jasim, O. Z., Hamed, N. H., & Abid, M. A. (2020). Urban Air Quality Assessment Using Integrated Artificial Intelligence Algorithms and Geographic Information System Modeling in a Highly Congested Area, Iraq. Journal of Southwest Jiaotong University, 55(1). https://doi.org/10.35741/issn.0258-2724.55.1.16 Jia, M., Cheng, X., Zhao, T., Yin, C., Zhang, X., Wu, X., Wang, L., & Zhang, R. (2019). Regional air quality forecast using a machine learning method and the WRF model over the yangtze river delta, east China. Aerosol and Air Quality Research, 19(7), 1602–1613. https://doi.org/10.4209/aaqr.2019.05.0275 Jiang, B., Xia, D., & Zhang, X. (2018). A multicomponent kinetic model established for investigation on atmospheric new particle formation mechanism in H2SO4-HNO3-NH3-VOC system. Science of the Total Environment, 616–617, 1414–1422. https://doi.org/10.1016/j.scitotenv.2017.10.174 Joharestani, M. Z., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019). PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere, 10(7). https://doi.org/10.3390/atmos10070373 Lauret, P., Heymes, F., Aprin, L., & Johannet, A. (2016). Atmospheric dispersion modeling using Artificial Neural Network based cellular automata. Environmental Modelling and Software, 85, 56–69. https://doi.org/10.1016/j.envsoft.2016.08.001 Lee, M., Lin, L., Chen, C. Y., Tsao, Y., Yao, T. H., Fei, M. H., & Fang, S. H. (2020). Forecasting Air Quality in Taiwan by Using Machine Learning. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-61151-7 Li, N., Chen, J. P., Tsai, I. C., He, Q., Chi, S. Y., Lin, Y. C., & Fu, T. M. (2016). Potential impacts of electric vehicles on air quality in Taiwan. Science of the Total Environment, 566–567, 919–928. https://doi.org/10.1016/j.scitotenv.2016.05.105 Liang, Y. C., Maimury, Y., Chen, A. H. L., & Juarez, J. R. C. (2020). Machine learning-based prediction of air quality. Applied Sciences (Switzerland), 10(24), 1–17. https://doi.org/10.3390/app10249151 Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., Amann, M., Anderson, H. R., Andrews, K. G., Aryee, M., Atkinson, C., Bacchus, L. J., Bahalim, A. N., Balakrishnan, K., Balmes, J., Barker-Collo, S., Baxter, A., Bell, M. L., Blore, J. D., … Ezzati, M. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2224–2260. https://doi.org/10.1016/S0140-6736(12)61766-8 Lin, C. Y., Lee, Y. H., Kuo, C. Y., Chen, W. C., Sheng, Y. F., & Su, C. J. (2018). Impact of river-dust events on air quality of western Taiwan during winter monsoon: Observed evidence and model simulation. Atmospheric Environment, 192, 160–172. https://doi.org/10.1016/j.atmosenv.2018.08.048 Liu, J., Chen, Y., Chao, S., Cao, H., Zhang, A., & Yang, Y. (2018). Emission control priority of PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective. Science of the Total Environment, 644, 20–30. https://doi.org/10.1016/j.scitotenv.2018.06.226 Lu, Q., Zheng, J., Ye, S., Shen, X., Yuan, Z., & Yin, S. (2013). Emission trends and source characteristics of SO2, NOx, PM10 and VOCs in the Pearl River Delta region from 2000 to 2009. Atmospheric Environment, 76, 11–20. https://doi.org/10.1016/j.atmosenv.2012.10.062 Lu, Z., Deng, S., Liu, X., Huang, L., Zhang, R., Song, H., & Li, G. (n.d.). Morphology and composition of particles emitted from conventional and alternative fuel vehicles. https://doi.org/10.1007/s11356-020-11671-6/Published Ma, J., Yu, Z., Qu, Y., Xu, J., & Cao, Y. (2020). Application of the xgboost machine learning method in pm2.5 prediction: A case study of shanghai. Aerosol and Air Quality Research, 20(1), 128–138. https://doi.org/10.4209/aaqr.2019.08.0408 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. https://doi.org/10.1016/j.procs.2020.03.258 Olcese, L. E., Palancar, G. G., & Toselli, B. M. (2015). A method to estimate missing AERONET AOD values based on artificial neural networks. Atmospheric Environment, 113, 140–150. https://doi.org/10.1016/j.atmosenv.2015.05.009 Olfert, J., & Rogak, S. (2019). Universal relations between soot effective density and primary particle size for common combustion sources. In Aerosol Science and Technology (Vol. 53, Issue 5, pp. 485–492). Taylor and Francis Inc. https://doi.org/10.1080/02786826.2019.1577949 Pisoni, E., Albrecht, D., Mara, T. A., Rosati, R., Tarantola, S., & Thunis, P. (2018). Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool. Atmospheric Environment, 183, 84–93. https://doi.org/10.1016/j.atmosenv.2018.04.006 Quan, J., Tie, X., Zhang, Q., Liu, Q., Li, X., Gao, Y., & Zhao, D. (2014). Characteristics of heavy aerosol pollution during the 2012-2013 winter in Beijing, China. Atmospheric Environment, 88, 83–89. https://doi.org/10.1016/j.atmosenv.2014.01.058 Ritz, B., Liew, Z., Yan, Q., Cuia, X., Virk, J., Ketzel, M., & Raaschou-Nielsen, O. (2018). Air pollution and autism in Denmark. Environmental Epidemiology, 2(4), e028. https://doi.org/10.1097/ee9.0000000000000028 Rodriguez, M. G., Heredia Rivera, B., Rodriguez Heredia, M., Rodriguez Heredia, B., & Gonzalez Segovia, R. (2019a). A study of dust airborne particles collected by vehicular traffic from the atmosphere of southern megalopolis Mexico City. Environmental Systems Research, 8(1). https://doi.org/10.1186/s40068-019-0143-3 Rodriguez, M. G., Heredia Rivera, B., Rodriguez Heredia, M., Rodriguez Heredia, B., & Gonzalez Segovia, R. (2019b). A study of dust airborne particles collected by vehicular traffic from the atmosphere of southern megalopolis Mexico City. Environmental Systems Research, 8(1). https://doi.org/10.1186/s40068-019-0143-3 Rönkkö, T., & Timonen, H. (2019). Overview of Sources and Characteristics of Nanoparticles in Urban Traffic-Influenced Areas. Journal of Alzheimer’s Disease, 72(1), 15–28. https://doi.org/10.3233/JAD-190170 Saravanan, R., & Sujatha, P. (2018). A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification. https://doi.org/10.1109/ICCONS.2018.8663155 Shou, Y., Huang, Y., Zhu, X., Liu, C., Hu, Y., & Wang, H. (2019). A review of the possible associations between ambient PM2.5 exposures and the development of Alzheimer’s disease. Ecotoxicology and Environmental Safety, 174, 344–352. https://doi.org/10.1016/j.ecoenv.2019.02.086 Soh, P. W., Chang, J. W., & Huang, J. W. (2018). Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations. IEEE Access, 6, 38186–38199. https://doi.org/10.1109/ACCESS.2018.2849820 Wang, S. H., Hung, R. Y., Lin, N. H., Gómez-Losada, Á., Pires, J. C. M., Shimada, K., Hatakeyama, S., & Takami, A. (2020). Estimation of background PM2.5 concentrations for an air-polluted environment. Atmospheric Research, 231. https://doi.org/10.1016/j.atmosres.2019.104636 Wang, Y. S., Chang, L. C., & Chang, F. J. (2021). Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan. Environmental Management, 67(1), 176–191. https://doi.org/10.1007/s00267-020-01391-5 Wang, Z., Lingzhi, Z., Zhang, Y., Zhou, Z., & Zhang, S. (2008). Morphology of single inhalable particle in the air polluted city of Shijiazhuang, China. In Journal of Environmental Sciences (Vol. 20). Wei, X., Gao, B., Wang, P., Zhou, H., & Lu, J. (2015). Pollution characteristics and health risk assessment of heavy metals in street dusts from different functional areas in Beijing, China. Ecotoxicology and Environmental Safety, 112, 186–192. https://doi.org/10.1016/j.ecoenv.2014.11.005 Wilhelm, M., Ghosh, J. K., Su, J., Cockburn, M., Jerrett, M., & Ritz, B. (2012). Traffic-related air toxics and term low birth weight in Los Angeles County, California. Environmental Health Perspectives, 120(1), 132–138. https://doi.org/10.1289/ehp.1103408 Wong, P. Y., Lee, H. Y., Zeng, Y. T., Chern, Y. R., Chen, N. T., Candice Lung, S. C., Su, H. J., & Wu, C. da. (2021). Using a land use regression model with machine learning to estimate ground level PM2.5. Environmental Pollution, 277. https://doi.org/10.1016/j.envpol.2021.116846 World Health Organization. (2018). World Health Statistics 2018 : monitoring health for the SDGs : sustainable development goals. Xing, Y. F., Xu, Y. H., Shi, M. H., & Lian, Y. X. (2016). The impact of PM2.5 on the human respiratory system. In Journal of Thoracic Disease (Vol. 8, Issue 1, pp. E69–E74). Pioneer Bioscience Publishing. https://doi.org/10.3978/j.issn.2072-1439.2016.01.19 Yang, Y., & Christakos, G. (2015). Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China). Environmental Science and Technology, 49(22), 13431–13438. https://doi.org/10.1021/acs.est.5b03614 Yu, X., Wong, M. S., Liu, C.-H., & Zhu, R. (2022). Synergistic data fusion of satellite observations and in-situ measurements for hourly PM2.5 estimation based on hierarchical geospatial long short-term memory. Atmospheric Environment, 286, 119257. https://doi.org/10.1016/j.atmosenv.2022.119257 Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., & Wang, Y. S. (2019a). Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. Journal of Cleaner Production, 209, 134–145. https://doi.org/10.1016/j.jclepro.2018.10.243 Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., & Wang, Y. S. (2019b). Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. Journal of Cleaner Production, 209, 134–145. https://doi.org/10.1016/j.jclepro.2018.10.243
指導教授	王聖翔(Sheng-Hsiang Wang)	審核日期	2023-2-3
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare