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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/131635
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/131635

    Title: 空氣品質感測網路的時間空間關聯模型
    Spatial-Temporal Correlation Modeling of Air Monitoring Sensor Network
    Authors: 蔡政憲
    Tsai, Zheng-Xian
    Contributors: 沈錳坤
    Shan, Man-Kwan
    Tsai, Zheng-Xian
    Keywords: 空氣品質估測
    Date: 2020
    Issue Date: 2020-09-02 12:16:12 (UTC+8)
    Abstract: 近年來,台灣的空氣汙染越來越嚴重,甚至已經開始影響到人的健康,因此針對空氣品質的監測和分析也就越來越重要。隨著無線感測網路技術的進步與發展,低成本微型感測器被採用並建構成大規模高密度的空氣品質監測網絡。但是低成本微型感測器在數據的穩定性上,容易產生大量的缺值。因此缺值問題對於大規模的低成本感測器網絡非常重要。
    In recent years, air pollution has become more and more serious in Taiwan. It is important to monitor and analyze air quality. With the development of wireless sensing network technology, low-cost sensors have been adopted to build the large-scale high-density air quality monitoring network. However, low-cost air quality sensors are suffered from the missing value problem. Estimation of missing values for low cost air quality sensors is essential for air quality monitoring network.
    This thesis targets at the machine learning approaches for estimation of missing values of low cost sensors. We investigate the correlation model that discovers the spatial-temporal relationship among sensors from historical data. The correlation model is utilized to estimate the air quality of the target sensor by corresponding neighbor sensors. Moreover, we also propose approaches to improve the effectiveness of the estimation algorithm. We consider the impact of wind on the diffusion of air pollution, and propose three different clustering strategies to group the PM2.5 time series and train the correlation model for each group individually. Experiments show that the proposed correlation model performs well and the proposed clustering strategy leads to prominent performance improvement. The mean absolute error (MAE) is as low as 3.2.
    Reference: [1] C. K. Chou et al., Seasonal Variations and Spatial Distribution of Carbonaceous Aerosols in Taiwan, Atmospheric Chemistry & Physics Discussions, Vol. 10, 7079-7113, 2010.
    [2] L. J. Chen, Y. H. Ho, H. H. Hsieh, S. T. Huang, H. C. Lee, and S. Mahajan, ADF: an Anomaly Detection Framework for Large-scale PM2.5 Sensing Systems, IEEE IoT Journal, Vol. 5, Issue. 2, 559 - 570, 2018.
    [3] C.H. Hsu and F.Y. Cheng, Classification of Weather Patterns to Study the Influence of Meteorological Characteristics on PM2.5 Concentrations in Yunlin County, Taiwan, Atmospheric Environment, Vol. 144,397-408, 2016.
    [4] H. D. He, M. Li, W. L. Wang, Z. Y. Wang, Y. Xue, Prediction of PM2. 5 Concentration Based on the Similarity in Air Quality Monitoring Network, Building and Environment, Vol. 137, 11 - 17, 2018.
    [5] Health Effects Institute, State of Global Air 2018: A Special Report on Global Exposure to Air Pollution and Its Disease Burden, Health Effects Institute, MA
    [6] C. R. Lin and M. S. Chen, On the Optimal Clustering of Sequential Data, IEEE International Conference on Data Mining, 2002.
    [7] Y. Lin et al., Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017.
    [8] Y. Lin et al., Exploiting Spatiotemporal Patterns for Accurate Air Quality Forecasting using Deep Learning, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018.
    [9] R. Manne, Analysis of Two Partial-Least-Squares Algorithms for Multivariate Calibration, Chemometrics Intelligent Laboratory Systems, Vol. 2, No. 1, 1987.
    [10] S. Mahajan, H. M. Liu, T. C. Tsai and L. J. Chen, Improving the Accuracy and Efficiency of PM2.5 Forecast Service Using Cluster-Based Hybrid Neural Network Model, IEEE Access, Vol. 6, 19193 - 19204, 2018.
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    [12] P. W. Soh, J. W. Chang, J. W. Huang, Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations, IEEE Access, Vol. 6, 38186 - 38199, 2018.
    [13] A. P. K. Tai et al., Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change, AGU Fall Meeting Abstracts, 2011.
    [14] Y. T. Tsai, Y. R. Zeng, and Y. S. Chang, Air Pollution Forecasting using RNN with LSTM, IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 2018
    [15] D. M. Westervelt et al., Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model. Atmospheric Environment, Vol. 142, page 43-56, 2016.
    [16] Z. Wong and Z. Long, PM2.5 Prediction Based on Neural Network, International Conference on Intelligent Computation Technology and Automation (ICICTA), 2018.
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    [18] B. Yang and Q. Chen, PM2.5 Concentration Estimation Based on Image Quality Assessment, ACPR, 2017.
    [19] L. Yan, Y. Wu, L. Yan, and M. Zhou, Encoder-Decoder Model for Forecast of PM2.5 Concentration per Hour, International Cognitive Cities Conference, 2018.
    [20] H. Zhu and X. Lu, The Prediction of PM2.5 Value Based on ARMA and Improved BP Neural Network Model, International Conference on Intelligent Networking and Collaborative Systems, 2016.
    [21] M. A. Zaytar and C. E. Amrani, Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks, International Journal of Computer Applications, Vol. 143. No. 11, 2016.
    [22] Y. Zheng et al., A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality, Microsoft Research Technical Report, 2014.
    [23] Y. Zhang et al., A Predictive Data Feature Exploration-Based Air Quality Prediction Approach. IEEE Access, Vol 7, 2019.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107753034
    Data Type: thesis
    DOI: 10.6814/NCCU202001442
    Appears in Collections:[資訊科學系] 學位論文

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