近年來，台灣地區的PM2.5空氣污染狀況日益嚴重。亞洲的大城市如北京、德里等也正面臨同樣的空污問題，引起了政府和專家學者的關注。由於亞洲地區的工業化、畜牧等人為活動，空氣污染的狀況更加嚴重，增加民眾罹患心血管疾病的機率。PM2.5的污染已經變成了現今社會不可忽視的問題。目前，官方氣象單位對於大氣動向的預測仍然使用傳統的統計模型。傳統的統計模型例如ARIMA對於時間序列資料的預測有一定的準確率。然而近年來隨著電腦和晶片計算能力的進步，神經網路和深度學習的應用領域也愈來愈廣。遞迴神經網路被發展來用於處理時間序列問題。長短期記憶模型擁有比遞迴神經網路更大時間幅度的記憶能力，同時也常被用於對現實中的序列資料進行預測分析。本論文使用長短期記憶模型預測未來的PM2.5小時平均濃度，希望政府和相關單位能針對此污染現象做出應對，改善空氣污染的問題。

Recently, pollution conditions of particulate matter 2.5 in Taiwan have become more severe day by day. Several other cities in Asia such as Beijing and Delhi are also facing the same pollution problem, which draws attention to government and experts. Due to the human activities in Asia such as industrialization and animal husbandry, air pollution condition has been getting worse, increases the possibility of population suffering from cardiovascular disease. Particular matter pollution has become a problem we cannot ignore in modern society. Currently, official meteorological department applies traditional statistic model to predict meteorology trend. Traditional statistic model such like ARIMA has certain accuracy on time series data. However, nowadays along with the calculate ability of computer and chips progressing, application field of neural network and deep learning has become much more extensive. Recurrent neural network had been developed to deal with time sequence data. Long short term memory model has a longer time range memorize ability than recurrent neural network, meanwhile has been frequently applied on forecasting and analyzation. This thesis utilizes the long short term memory model to predict future particular matter hourly average concentration, in hope that government and the departments concerned could take actions on the pollution phenomenon, improve the air pollution problem.

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