Applying PCA to Deep Learning Forecasting Models for Predicting PM 2.5

Sang Won Choi and Brian H. S. Kim
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Sang Won Choi: Department of Agricultural Economics and Rural Development, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea
Brian H. S. Kim: Department of Agricultural Economics and Rural Development, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea

Sustainability, 2021, vol. 13, issue 7, 1-30

Abstract: Fine particulate matter (PM 2.5 ) is one of the main air pollution problems that occur in major cities around the world. A country’s PM 2.5 can be affected not only by country factors but also by the neighboring country’s air quality factors. Therefore, forecasting PM 2.5 requires collecting data from outside the country as well as from within which is necessary for policies and plans. The data set of many variables with a relatively small number of observations can cause a dimensionality problem and limit the performance of the deep learning model. This study used daily data for five years in predicting PM 2.5 concentrations in eight Korean cities through deep learning models. PM 2.5 data of China were collected and used as input variables to solve the dimensionality problem using principal components analysis (PCA). The deep learning models used were a recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). The performance of the models with and without PCA was compared using root-mean-square error (RMSE) and mean absolute error (MAE). As a result, the application of PCA in LSTM and BiLSTM, excluding the RNN, showed better performance: decreases of up to 16.6% and 33.3% in RMSE and MAE values. The results indicated that applying PCA in deep learning time series prediction can contribute to practical performance improvements, even with a small number of observations. It also provides a more accurate basis for the establishment of PM 2.5 reduction policy in the country.

Keywords: principal components analysis (PCA); PM 2.5; recurrent neural network RNN); long short-term memory (LSTM); bidirectional LSTM (BiLSTM); deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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