A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Abstract

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

Deep-learning architecture for PM2.5 concentration prediction: A review

“…[ 46 ] 2020 Taiwan, China LSTM H/S/T+1 4.46 - 30.00 0.86 Park et al. [ 47 ] 2021 Seoul, South Korea LSTM H/S/T+3 - - - - Mengara et al. [ 56 ] 2022 Seoul, South Korea AE + BiLSTM H/S/T+1 7.48 5.02 30.48 - Mengara et al.…”

oneM2M-Enabled Prediction of High Particulate Matter Data Based on Multi-Dense Layer BiLSTM Model

“…We have predicted PM2.5 concentrations for the three-day dataset; the experiments used three parameters to assess the efficacy of the proposed model: mean square error (MSE) (8), mean absolute error (MAE) (9), and mean absolute percentage error (MAPE) (10) as metrics to appraise the achievement of the Multi-Dense Layer BiLSTM model:…”

“…Ting Tsai et al [7] employed the RNN model to predict PM2.5 concentrations, but the result of errors such as RMSE and MAE are high. Park et al [8] used the long short-term memory (LSTM) and artificial neural network (ANN) models to forecast PM, which had a higher F-1 score than the individual scores of LSTM, ANN, and random forest (RF) models. Huang et al [9] forecasted PM2.5 in a smart city environment using a deep neural network (APNet) based on CNN-LSTM.…”

Comparative Analysis of Predictive Models for Fine Particulate Matter in Daejeon, South Korea

“…Several authors have proposed machine learning-based and deep learning-based methods for predicting the AQI using meteorological data [16,[20][21][22][23][24]. For example, Park et al [16] predicted PM 2.5 concentrations on the basis of meteorological features, including temperature, humidity, wind direction, and wind speed. A dataset was collected from two areas in Seoul, South Korea.…”