Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

References

• Abdul-Wahab, S. A., & Al-Alawi, S. M. (2002). Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling and Software, 17(3), 219–228. https://doi.org/10.1016/S1364-8152(01)00077-9
   Web of Science ®Google Scholar
• Abdullah, S., Nasir, N. H. A., Ismail, M., Ahmed, A. N., & Jarkoni, M. N. K. (2019). Development of ozone prediction model in urban area. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2263–2267. https://doi.org/10.35940/ijitee.J1127.0881019
   Google Scholar
• Ali Ghorbani, M., Kazempour, R., Chau, K.-W., Shamshirband, S., & Ghazvinei, P. T. (2018). Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, Northern Iran. Engineering Applications of Computational Fluid Mechanics, 12(1), 724–737. https://doi.org/10.1080/19942060.2018.1517052
   Web of Science ®Google Scholar
• Alimissis, A., Philippopoulos, K., Tzanis, C. G., & Deligiorgi, D. (2018). Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric Environment, 191, 205–213.https://doi.org/10.1016/j.atmosenv.2018.07.058
   Web of Science ®Google Scholar
• Antanasijević, D., Pocajt, V., Perić-Grujić, A., & Ristić, M. (2019). Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks. Environmental Pollution, 244, 288–294. https://doi.org/10.1016/j.envpol.2018.10.051
   PubMed Web of Science ®Google Scholar
• Arsić, M., Mihajlović, I., Nikolić, D., Živković, Ž., & Panić, M. (2020). Prediction of ozone concentration in ambient air using multilinear regression and the artificial neural networks methods. Ozone: Science & Engineering, 42(1), 79–88. https://doi.org/10.1080/01919512.2019.1598844
   Web of Science ®Google Scholar
• Banja, M., Papanastasiou, D. K., Poupkou, A., & Melas, D. (2012). Development of a short-term ozone prediction tool in tirana area based on meteorological variables. Atmospheric Pollution Research, 3(1), 32–38.https://doi.org/10.5094/APR.2012.002
   Web of Science ®Google Scholar
• Biancofiore, F., Verdecchia, M., Di Carlo, P., Tomassetti, B., Aruffo, E., Busilacchio, M., Bianco, S., Di Tommaso, S., & Colangeli, C. (2015). Analysis of surface ozone using a recurrent neural network. Science of the Total Environment, 514, 379–387. https://doi.org/10.1016/j.scitotenv.2015.01.106
   PubMed Web of Science ®Google Scholar
• Capilla, C. (2016). Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models. International Journal of Sustainable Development and Planning, 11(4), 558–565. https://doi.org/10.2495/sdp-v11-n4-558-565
   Google Scholar
• Chen, S., Mihara, K., & Wen, J. (2018). Time series prediction of CO2, TVOC and HCHO based on machine learning at different sampling points. Building and Environment, 146, 238–246. https://doi.org/10.1016/j.buildenv.2018.09.054
   Web of Science ®Google Scholar
• Ezimand, K., & Kakroodi, A. A. (2019). Prediction and spatio – Temporal analysis of ozone concentration in a metropolitan area. Ecological Indicators, 103, 589–598. https://doi.org/10.1016/j.ecolind.2019.04.059
   Web of Science ®Google Scholar
• Fang, C., Wang, L., & Wang, J. (2019). Analysis of the spatial–temporal variation of the surface ozone concentration and its associated meteorological factors in Changchun. Environments, 6(4), 46. https://doi.org/10.3390/environments6040046
   Web of Science ®Google Scholar
• Gao, M., Yin, L., & Ning, J. (2018). Artificial neural network model for ozone concentration estimation and Monte Carlo analysis. Atmospheric Environment, 184, 129–139. https://doi.org/10.1016/j.atmosenv.2018.03.027
   Web of Science ®Google Scholar
• Gómez-Losada, Á., Asencio–Cortés, G., Martínez–Álvarez, F., & Riquelme, J. C. (2018). A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information. Environmental Modelling and Software, 110, 52–61. https://doi.org/10.1016/j.envsoft.2018.08.013
   Web of Science ®Google Scholar
• Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Vila-Francés, J., Carrasco, J. L., & del Valle-Tascón, S. (2006). Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration. Atmospheric Environment, 40(32), 6173–6180. https://doi.org/10.1016/j.atmosenv.2006.04.067
   Web of Science ®Google Scholar
• Gradišar, D., Grašič, B., Zlata Božnar, M., Mlakar, P., & Kocijan, J. (2016). Improving of local ozone forecasting by integrated models. Environmental Science and Pollution Research, 23(18), 18439–18450. https://doi.org/10.1007/s11356-016-6989-2
   PubMed Web of Science ®Google Scholar
• Hájek, P., & Olej, V. (2012). Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty. Ecological Informatics, 12, 31–42. https://doi.org/10.1016/j.ecoinf.2012.09.001
   Web of Science ®Google Scholar
• Homsi, R., Shiru, M. S., Shahid, S., Ismail, T., Harun, S. B., Al-Ansari, N., Chau, K.-W., & Yaseen, Z. M. (2019). Precipitation projection using a CMIP5 GCM ensemble model: A regional investigation of Syria. Engineering Applications of Computational Fluid Mechanics, 14(1), 90–106. https://doi.org/10.1080/19942060.2019.1683076
   Web of Science ®Google Scholar
• Hoshyaripour, G., Brasseur, G., Andrade, M. F., Gavidia-Calderón, M., Bouarar, I., & Ynoue, R. Y. (2016). Prediction of ground-level ozone concentration in São Paulo, Brazil: Deterministic versus statistic models. Atmospheric Environment, 145, 365–375. https://doi.org/10.1016/j.atmosenv.2016.09.061
   Web of Science ®Google Scholar
• Huang, Y., Yang, Z., & Gao, Z. (2019). Contributions of indoor and outdoor sources to ozone in residential buildings in Nanjing. International Journal of Environmental Research and Public Health, 16(14), 2587. https://doi.org/10.3390/ijerph16142587
   Web of Science ®Google Scholar
• Ismail, M., Suroto, A., Ibrahim, T. A. E., & Abdullah, A. M. (2011). Tropospheric ozone trend in the Muda irrigation area, Kedah. Journal of Physical Science, 22(2), 87–99.
   Google Scholar
• Li, Y., Jiang, P., She, Q., & Lin, G. (2018). Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine. Environmental Pollution, 241, 1115–1127. https://doi.org/10.1016/j.envpol.2018.05.072
   PubMed Web of Science ®Google Scholar
• Luna, A. S., Paredes, M. L. L., de Oliveira, G. C. G., & Corrêa, S. M. (2014). Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmospheric Environment, 98, 98–104.https://doi.org/10.1016/j.atmosenv.2014.08.060
   Web of Science ®Google Scholar
• Noori, R., Hoshyaripour, G., Ashrafi, K., & Araabi, B. N. (2010). Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44(4), 476–482. https://doi.org/10.1016/j.atmosenv.2009.11.005
   Web of Science ®Google Scholar
• “Official Portal of Department of Environment.” (2019).
   Google Scholar
• Pak, U., Kim, C., Ryu, U., Sok, K., & Pak, S. (2018). A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere and Health, 11(8), 883–895. https://doi.org/10.1007/s11869-018-0585-1
   Web of Science ®Google Scholar
• Pawlak, I., & Jarosławski, J. (2019). Forecasting of surface ozone concentration by using artificial neural networks in rural and urban areas in Central Poland. Atmosphere, 10(2), 52. https://doi.org/10.3390/atmos10020052
   Web of Science ®Google Scholar
• Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K.-W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187. https://doi.org/10.1080/19942060.2018.1564702
   Web of Science ®Google Scholar
• Ragosta, M., D’Emilio, M., & Giorgio, G. A. (2015). Input strategy analysis for an air quality data modelling procedure at a local scale based on Neural Network. Environmental Monitoring and Assessment, 187(5). https://doi.org/10.1007/s10661-015-4556-9
   PubMed Web of Science ®Google Scholar
• Shaban, K. B., Kadri, A., & Rezk, E. (2016). Urban air pollution monitoring system with forecasting models. IEEE Sensors Journal, 16(8), 2598–2606. https://doi.org/10.1109/JSEN.2016.2514378
   Web of Science ®Google Scholar
• Shen, J., Chen, J., Zhang, X., Zou, S., & Gao, Z. (2017). Outdoor and indoor ozone concentration estimation based on artificial neural network and single zone mass balance model. Procedia Engineering, 205, 1835–1842. https://doi.org/10.1016/j.proeng.2017.10.253
   Google Scholar
• Zhan, Y., Luo, Y., Deng, X., Grieneisen, M. L., Zhang, M., & Di, B. (2018). Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environmental Pollution, 233, 464–473. https://doi.org/10.1016/j.envpol.2017.10.029
   PubMed Web of Science ®Google Scholar
• Zhang, J., Wang, C., Qu, K., Ding, J., Shang, Y., Liu, H., & Wei, M. (2019). Characteristics of ozone pollution, regional distribution and causes during 2014–2018 in Shandong Province, East China. Atmosphere, 10(9), 501. https://doi.org/10.3390/atmos10090501
   Web of Science ®Google Scholar