Air Pollution Forecasting using LSTM with Aggregation Model

• Main Authors: TSAI, YI-TING, 蔡宜廷
• Other Authors: CHANG, YUE-SHAN
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/5p88t6

碩士 === 國立臺北大學 === 資訊工程學系 === 107 === In developed countries or developing countries, the effects of air pollutants on the health of the public are consistent. PM2.5 is a suspended particle In the airborne particulate pollutants. There is no impact on the human body for the concentration threshold of suspended particulates. The concentration of suspended particulates is for humans with diseases of the respiratory system. The impact is also different, so no standard or regulation can completely protect the public. Therefore, predicting the value of PM2.5 in the future is an important issue. This paper uses data provided by the Environmental Protection Agency and Central Weather Bureau from 2013 to 2017. Dividing a data set into data sets of three different sources of pollution. The Aggregation Model uses three types time series data sets of different pollution sources to establish three LSTM (Long Short-Term Memory networks) sub-neural networks to obtain prediction characteristics in three different pollution sources. Combining the predicted features produced by the three sub-neural networks, and outputting prediction feature to the fully connected layer, respectively, giving different weights by the hidden layers from back propagation, and finally obtaining the PM2.5 values predicted in the next 1 to 8 hours. After comparing with the existing methods ANN and LSTM, the accuracy of the next hour is 0.15 less than the LSTM and 0.11 in the MAE. The RMSE is reduced by 0.75 and the MAE error is reduced by 0.54 after comparison with the ANN.