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http://dx.doi.org/10.9717/kmms.2022.25.2.440

Exploiting Neural Network for Temporal Multi-variate Air Quality and Pollutant Prediction  

Khan, Muneeb A. (Dept. of Software, Sangmyung University)
Kim, Hyun-chul (Dept. of Software, Sangmyung University)
Park, Heemin (Dept. of Software, Sangmyung University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.2, 2022 , pp. 440-449 More about this Journal
Abstract
In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).
Keywords
Air Quality Index (AQI); Air Pollutant; Temporal clustering; Forecasting;
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