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Prediction of Fine Dust in Gyeonggi-do Industrial Complex using Machine Learning Methods
Dong-Jun Won, Sun-Kyum Kim, Yeonghun Kim, Gyuwon Song
http://doi.org/10.5626/JOK.2021.48.7.764
Recently, research on fine dust has been conducted through various prediction techniques. However, currently the research focused on PM10 concentration prediction, and thus it is necessary to develop a model capable of predicting PM2.5 concentration. In this paper, we have collected air quality, weather, and traffic of the Banwol Shihwa National Industrial Complex in the recent two years. The significance of the variable been identified through correlation analysis and regression analysis among PM2.5 and PM10, SO₂, NO₂, CO, O₃, temperature, humidity, wind direction, wind speed, precipitation, road section vehicle speed for each vehicle. Next, the data has been used to predict PM2.5 concentration based on time in the industrial complex. Through the artificial intelligence techniques, Random Forest, XGBoost, LightGBM, Deep neural network and Voting models, PM2.5 concentration industrial complexes been predicted on an hourly basis, and comparative analysis been conducted based on RMSE. As a result of prediction, RMSE was 6.27, 6.41, 6.22, 6.64, and 6.12, respectively, and each technique showed very high performance compared to 10.77 of the technique predicted by Air Korea.
A Deep Learning LSTM Framework for Urban Traffic Flow and Fine Dust Prediction
Hongsuk Yi, Khac-Hoai Nam Bui, Choong-Nyoung Seon
http://doi.org/10.5626/JOK.2020.47.3.292
Accurate and timely forecasting is an essential step for the successful deployment of smart cities. With the rapid growth of traffic data collected daily, recent studies have focused on deep learning based on long-term short term memory (LSTM) for short-term traffic prediction, especially in urban areas. However, the short-term (five minutes) LSTM model is limited in the real-time nonlinear traffic flow prediction. Moreover, the fine dust prediction based on traffic data is also an emerging issue in this research area. Thus, this paper designs the multiple traffic data-based multi-input/output LSTM framework for supporting medium and long-term prediction. Additionally, a convolutional LSTM (ConvLSTM) model is developed for predicting fine dust flow based on traffic data. Regarding the experiment, we analyzed data from the Vehicle Detection System (VDS) located on major roads in Daejeon City for the evaluation. The experiment indicates promising results for the proposed approach.
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