An Effective Hybrid Model for Predicting Air Quality of Ankara

Year 2024, , 1 - 8, 29.06.2024

Anıl Utku , Ümit Can

https://doi.org/10.46572/naturengs.1411983

Abstract

Increasing industrialization, population growth, urbanization and increase in fossil fuel consumption lead to air pollution that affects human health by polluting the atmosphere. Particulate matter, known as PM10 and PM2.5, are air pollutants that can remain suspended in the air in solid, liquid or both states. Substances are described according to their aerodynamic diameter, known as particle size. Estimating particulate matter concentrations is very important for human health and the environment. In this study, a hybrid deep learning model was developed for air quality prediction using PM2.5 and PM10 concentration data obtained from Bahçelievler, Demetevler, Sincan and Törekent air quality monitoring stations in Ankara. In the developed model, it was aimed to use the successful features of CNN and LSTM models. The developed CNN-LSTM model was compared with LR, RF, SVM, MLP, CNN and LSTM using MSE, RMSE, R2, and MAE. Experimental results showed that the CNN-LSTM model outperformed the compared models and each station had an R2 of approximately 0.9.

Keywords

CNN, LSTM, PM10, PM2.5.

References

• Harishkumar, K. S., Yogesh, K. M., & Gad, I. (2020). Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models. Procedia Computer Science, 171, 2057-2066.
• Kalia, P., & Ansari, M. A. (2020). IOT based air quality and particulate matter concentration monitoring system. Materials Today: Proceedings, 32, 468-475.
• Pio, C., Rienda, I. C., Nunes, T., Gonçalves, C., Tchepel, O., Pina, N. K., ... & Alves, C. A. (2022). Impact of biomass burning and non-exhaust vehicle emissions on PM10 levels in a mid-size non-industrial western Iberian city. Atmospheric Environment, 289, 119293.
• Zoran, M. A., Savastru, R. S., Savastru, D. M., & Tautan, M. N. (2020). Assessing the relationship between surface levels of PM2. 5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. Science of the total environment, 738, 139825.
• Meo, S. A., Almutairi, F. J., Abukhalaf, A. A., & Usmani, A. M. (2021). Effect of green space environment on air pollutants PM2. 5, PM10, CO, O3, and incidence and mortality of SARS-CoV-2 in highly green and less-green countries. International Journal of Environmental Research and Public Health, 18(24), 13151.
• Olabi, A. G., Obaideen, K., Elsaid, K., Wilberforce, T., Sayed, E. T., Maghrabie, H. M., & Abdelkareem, M. A. (2022). Assessment of the pre-combustion carbon capture contribution into sustainable development goals SDGs using novel indicators. Renewable and Sustainable Energy Reviews, 153, 111710.
• Lutz, É., & Coradi, P. C. (2022). Applications of new technologies for monitoring and predicting grains quality stored: Sensors, internet of things, and artificial intelligence. Measurement, 188, 110609.
• Liu, X., Lu, D., Zhang, A., Liu, Q., & Jiang, G. (2022). Data-driven machine learning in environmental pollution: gains and problems. Environmental science & technology, 56(4), 2124-2133.
• Utku, A., Can, Ü., Kamal, M., Das, N., Cifuentes-Faura, J., & Barut, A. (2023). A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction. Atmospheric Pollution Research, 14(8), 101836.
• Kristiani, E., Kuo, T. Y., Yang, C. T., Pai, K. C., Huang, C. Y., & Nguyen, K. L. P. (2021). PM2. 5 forecasting model using a combination of deep learning and statistical feature selection. IEEE Access, 9, 68573-68582.
• Peng, J., Han, H., Yi, Y., Huang, H., & Xie, L. (2022). Machine learning and deep learning modeling and simulation for predicting PM2. 5 concentrations. Chemosphere, 308, 136353.
• Zhang, Q., Wu, S., Wang, X., Sun, B., & Liu, H. (2020). A PM2. 5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations. Journal of cleaner production, 275, 122722.
• Czernecki, B., Marosz, M., & Jędruszkiewicz, J. (2021). Assessment of machine learning algorithms in short-term forecasting of pm10 and pm2. 5 concentrations in selected polish agglomerations. Aerosol and Air Quality Research, 21(7), 200586.
• Menares, C., Perez, P., Parraguez, S., & Fleming, Z. L. (2021). Forecasting PM2. 5 levels in Santiago de Chile using deep learning neural networks. Urban Climate, 38, 100906.
• Harishkumar, K. S., Yogesh, K. M., & Gad, I. (2020). Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models. Procedia Computer Science, 171, 2057-2066.
• https://ulasav.csb.gov.tr/dataset/06-hava-kalitesi-verileri
• Wang, Q., & Wang, L. (2020). Renewable energy consumption and economic growth in OECD countries: A nonlinear panel data analysis. Energy, 207, 118200.
• Wang, Q., Yang, T., & Li, R. (2023). Does income inequality reshape the environmental Kuznets curve (EKC) hypothesis? A nonlinear panel data analysis. Environmental Research, 216, 114575.
• Band, S. S., Janizadeh, S., Chandra Pal, S., Saha, A., Chakrabortty, R., Melesse, A. M., & Mosavi, A. (2020). Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing, 12(21), 3568.
• Su, Y., Weng, K., Lin, C., & Zheng, Z. (2021). An improved random forest model for the prediction of dam displacement. IEEE Access, 9, 9142-9153.
• Ray, S. (2019, February). A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). IEEE.
• Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215.
• Taşkın, G., & Camps-Valls, G. (2021). Graph embedding via high dimensional model representation for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11.
• Zhang, J., Li, C., Yin, Y., Zhang, J., & Grzegorzek, M. (2023). Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artificial Intelligence Review, 56(2), 1013-1070.
• Xiao, X., Liu, J., Liu, D., Tang, Y., Dai, J., & Zhang, F. (2021). SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines. Concurrency and Computation: Practice and Experience, 33(17), e6315.
• Wang, J., Wu, H., Zhang, X., & Yao, Y. (2020). Watermarking in deep neural networks via error back-propagation. Electronic Imaging, 2020(4), 22-1.
• Mishra, M. (2021). Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies. Journal of Cultural Heritage, 47, 227-245.
• Salehi, A. W., Khan, S., Gupta, G., Alabduallah, B. I., Almjally, A., Alsolai, H., ... & Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. Sustainability, 15(7), 5930.
• Weerakody, P. B., Wong, K. W., Wang, G., & Ela, W. (2021). A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing, 441, 161-178.
• Harrou, F., Kadri, F., & Sun, Y. (2020). Forecasting of photovoltaic solar power production using LSTM approach. Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems, 3.