Design and Analysis of Air Pollution Concentration Prediction Models Using Transfer Learning and Recurrent Neural Networks
Keywords:
Transfer learning, Recurrent Neural Network
Abstract
Since air pollution (AP) poses a serious risk to human health, many people have started paying greater attention to it in recent years. Precise Air Pollution prediction helps individuals schedule their outdoor activities and contributes to human health protection. In this study, recurrent neural networks (RNNs) with long short-term memory (LSTM) were used to predict Macau's future APS concentration. Data on the concentration of APS as well as environmental data have also been used. Additionally, some air quality monitoring stations (AQMSs) in Macau have fewer overall observed data while simultaneously collecting less observed data for specific APS kinds. In order to help AQMSs with less observed data, transfer learning, and pre-trained neural networks have been utilized. The purpose of this study is to show how a collection of neural network algorithms has been utilized for these two pollutant elements. The approach is given considerable thought in this paper, and datasets regarding air and water pollution as well as expected parameters were additionally collected for future development efficiency.
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References
Asha B. Chelani et al, Mohammad M, Al-Sultan A (2020) A new method for prediction of air pollution based on intelligent computation. Soft Computing 24(1):661–680
C. Zhang, L. Di, Z. Sun, L. Lin, E. G. Yu, and J. Gaigalas, “Exploring cloud-based web processing service: a case study on the implementation of CMAQ as a service,” Environmental Modelling & Software, vol. 113, pp. 29–41, 2019.
Asha B. Chelani et al, C. Gao et al., “Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1 CAMx v6.50 CHIMERE v2017r4 and WRFChem(v3.9.1),” Geoscientific Model Development, vol. 12, no. 11, pp. 4603–4625, 2019.
Y. Bai, Y. Li, B. Zeng, C. Li, and J. Zhang, “Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality,” Journal of Cleaner Production, vol. 224, pp. 739–750, 2019.
J. Zhu, P. Wu, H. Chen, L. Zhou, and Z. Tao, “A hybrid forecasting approach to air quality time series based on endpoint condition and combined forecasting model,” International
Journal of Environmental Research and Public Health, vol. 15, no. 9, p. 1941, 2018.
M. Ðuri´c and D. Vujovi´c, “Short-term forecasting of air pollution index in Belgrade Serbia,” Meteorological Applications, vol. 27, p. 5, 2020.
A. Glowacz, “Fault diagnosis of electric impact drills using thermal imaging,” Measurement, vol. 171, Article ID 108815, 2021.
Y. Zhang, B. Chen, G. Pan, and Y. Zhao, “A novel hybrid model based on VMD-WTand PCA-BP-RBF neural network for short-term wind speed forecasting,” Energy Conversion and Management, vol. 195, pp. 180–197, 2019.
J. Chen, J. Lu, J. C. Avise, J. A. DaMassa, M. J. Kleeman, and A. P. Kaduwela, “Seasonal modeling of PM2.5 in California’s san joaquin valley,” Atmospheric Environment, vol. 92, pp. 182–190, 2014.
D. R. Liu, S. J. Lee, Y. Huang, and C. J. Chiu, “Air pollution forecasting based on attention-based LSTM neural network and ensemble learning,” Expert Systems, vol. 37, Article ID e12511, 2020.
Swathi, P. (2021). Industry Applications of Augmented Reality and Virtual Reality. Journal of Artificial Intelligence, Machine Learning and Neural Network (JAIMLNN) ISSN: 2799-1172, 1(02), 14-18.
Swathi, P. (2013). Scope of Financial Management and Functions of Finance. International Journal of Advanced in Management, Technology and Engineering Sciences. ISSN NO-2249-7455, 3, 109-116.
Swathi, P. (2022). Implications For Research In Artificial Intelligence. Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) ISSN: 2799-1156, 2(02), 25-28.
Swathi, P. (2022). A Study On The Restrictions Of Deep Learning. Journal Of Artificial Intelligence, Machine Learning and Neural Networks, ISSN-2799-1172, 2(2), 57-61.
Bhaskar, N., Ramana, S., & Kumar, G. M. (2023, January). Internet of Things for Green Smart City Application Based on Biotechnology Techniques. In 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1-7). IEEE.
Ramana, S., Bhaskar, N., Murthy, M. R., & Sharma, M. R. (2023). Machine Learning for a Payment Security Evaluation System for Mobile Networks. In Intelligent Communication Technologies and Virtual Mobile Networks (pp. 347-356). Singapore: Springer Nature Singapore.
Ramana, S., Bhaskar, N., & Murthy, M. R. (2020). Three Level Gateway protocol for secure M-Commerce Transactions. Solid State Technology, 63(6), 11155-11174.
Pothuganti, K. (2021). Long Short-Term Memory (LSTM) algorithm based prediction of stock market exchange. International Journal of Research Publication and Reviews, 2(1), 90-93.
Pothuganti, K. (2021). Long Short-Term Memory (LSTM) algorithm based prediction of stock market exchange. International Journal of Research Publication and Reviews, 2(1), 90-93.
C. Zhang, L. Di, Z. Sun, L. Lin, E. G. Yu, and J. Gaigalas, “Exploring cloud-based web processing service: a case study on the implementation of CMAQ as a service,” Environmental Modelling & Software, vol. 113, pp. 29–41, 2019.
Asha B. Chelani et al, C. Gao et al., “Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1 CAMx v6.50 CHIMERE v2017r4 and WRFChem(v3.9.1),” Geoscientific Model Development, vol. 12, no. 11, pp. 4603–4625, 2019.
Y. Bai, Y. Li, B. Zeng, C. Li, and J. Zhang, “Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality,” Journal of Cleaner Production, vol. 224, pp. 739–750, 2019.
J. Zhu, P. Wu, H. Chen, L. Zhou, and Z. Tao, “A hybrid forecasting approach to air quality time series based on endpoint condition and combined forecasting model,” International
Journal of Environmental Research and Public Health, vol. 15, no. 9, p. 1941, 2018.
M. Ðuri´c and D. Vujovi´c, “Short-term forecasting of air pollution index in Belgrade Serbia,” Meteorological Applications, vol. 27, p. 5, 2020.
A. Glowacz, “Fault diagnosis of electric impact drills using thermal imaging,” Measurement, vol. 171, Article ID 108815, 2021.
Y. Zhang, B. Chen, G. Pan, and Y. Zhao, “A novel hybrid model based on VMD-WTand PCA-BP-RBF neural network for short-term wind speed forecasting,” Energy Conversion and Management, vol. 195, pp. 180–197, 2019.
J. Chen, J. Lu, J. C. Avise, J. A. DaMassa, M. J. Kleeman, and A. P. Kaduwela, “Seasonal modeling of PM2.5 in California’s san joaquin valley,” Atmospheric Environment, vol. 92, pp. 182–190, 2014.
D. R. Liu, S. J. Lee, Y. Huang, and C. J. Chiu, “Air pollution forecasting based on attention-based LSTM neural network and ensemble learning,” Expert Systems, vol. 37, Article ID e12511, 2020.
Swathi, P. (2021). Industry Applications of Augmented Reality and Virtual Reality. Journal of Artificial Intelligence, Machine Learning and Neural Network (JAIMLNN) ISSN: 2799-1172, 1(02), 14-18.
Swathi, P. (2013). Scope of Financial Management and Functions of Finance. International Journal of Advanced in Management, Technology and Engineering Sciences. ISSN NO-2249-7455, 3, 109-116.
Swathi, P. (2022). Implications For Research In Artificial Intelligence. Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) ISSN: 2799-1156, 2(02), 25-28.
Swathi, P. (2022). A Study On The Restrictions Of Deep Learning. Journal Of Artificial Intelligence, Machine Learning and Neural Networks, ISSN-2799-1172, 2(2), 57-61.
Bhaskar, N., Ramana, S., & Kumar, G. M. (2023, January). Internet of Things for Green Smart City Application Based on Biotechnology Techniques. In 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1-7). IEEE.
Ramana, S., Bhaskar, N., Murthy, M. R., & Sharma, M. R. (2023). Machine Learning for a Payment Security Evaluation System for Mobile Networks. In Intelligent Communication Technologies and Virtual Mobile Networks (pp. 347-356). Singapore: Springer Nature Singapore.
Ramana, S., Bhaskar, N., & Murthy, M. R. (2020). Three Level Gateway protocol for secure M-Commerce Transactions. Solid State Technology, 63(6), 11155-11174.
Pothuganti, K. (2021). Long Short-Term Memory (LSTM) algorithm based prediction of stock market exchange. International Journal of Research Publication and Reviews, 2(1), 90-93.
Pothuganti, K. (2021). Long Short-Term Memory (LSTM) algorithm based prediction of stock market exchange. International Journal of Research Publication and Reviews, 2(1), 90-93.
Published
2023-08-31
How to Cite
Mankala Satish, & Peddi Niranjan Reddy. (2023). Design and Analysis of Air Pollution Concentration Prediction Models Using Transfer Learning and Recurrent Neural Networks. Central Asian Journal of Theoretical and Applied Science, 4(8), 135-142. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1269
Section
Articles
