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https://nccur.lib.nccu.edu.tw/handle/140.119/131635
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| 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
蔡政憲
Tsai, Zheng-Xian |
| Keywords: | 空氣品質估測
缺值
感測器網路
低成本裝置 |
| 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。
本論文針對微型感測器的缺值問題，研究由歷史資料中學習感測器之間的時間及空間關係的關聯模型。進而運用關聯模型，由鄰居感測器來估測感測器的空氣品質，藉此填補目標感測器的缺值。此外，我們也提出改進的方法，以提升估測的效果。我們考量風力對於空汙擴散的影響，我們提出三種不同的分群策略，將PM2.5時間序列的資料分群，分別訓練關聯模型。實驗顯示我們所提出的關聯模型有顯著的估測效果。而我們所提出的分群策略有明顯的效果改進，平均絕對誤差(MAE)約3.2。
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. |
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[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.
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| Description: | 碩士
國立政治大學
資訊科學系
107753034 |
| 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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