PM 2 5 Prediction based on LSTM Model

論文翻譯標題: 基於長短期記憶模型的懸浮微粒2 5預測
  • 林 彥良

學生論文: Master's Thesis


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
獎項日期2018 八月 15
監督員Teh-Lu Liao (Supervisor)