پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شمارهها | 6,244 |
| تعداد مقالات | 67,922 |
| تعداد مشاهده مقاله | 115,658,057 |
| تعداد دریافت فایل اصل مقاله | 90,422,526 |
Prediction of Air Pollutants Concentration Emitted from Kirkuk Cement Plant Based on Deep Learning and Gaussian Equation Outputs | ||
| Pollution | ||
| دوره 9، شماره 2، تیر 2023، صفحه 810-820 اصل مقاله (1.48 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/poll.2023.352186.1714 | ||
| نویسندگان | ||
| Qayssar Mahmood Ajaj1؛ Helmi Zulhaidi Mohd Shafri* 1؛ Mohammad Firuz Ramli2؛ Aimrun Wayayok3 | ||
| 1Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia | ||
| 2Department of Environmental Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia | ||
| 3Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia | ||
| چکیده | ||
| Researchers are interested in developing techniques to monitor, manage and predict the risks of gases and particles emitted from cement factories, which have a direct and negative impact on human health. Deep learning (DL) is a critical component of data mining, which further involves statistics and prediction. In this study, we developed a deep learning prediction model called the Deep Pollutant Prediction Model (DPPM). The data used for DPPM are separated into two types: observed data from a pollution monitoring station of the Institute of Mental Health in Ahmedabad City, India coded as (GJ001), to validate the model and simulated data generated using the Gaussian Plume Model for the hypothetical receptor (Laylan District, Kirkuk, Iraq) to predict the pollution that emitted from Kirkuk Cement Plant 5 km apart from the study area. The findings indicated that the DPPM has high efficiency in both Allahabad and Laylan stations, with more closed results for the data in the Laylan station, which is based on the Gaussian equation simulated data. Since the highest loss function value in the Laylan is 0.0221 of the CaO parameter, while it is 4.466 of the AQI parameter for the Allahabad Station, and the smallest loss function value in the Laylan is equal to 0.0041of both Fe2O3 and MgO parameters, it corresponds to 0.038 of Xylene for the Allahabad station. The results of the study proved that data continuity and non-volatility produce excellent outcomes for DPPM. | ||
| کلیدواژهها | ||
| Deep learning (DL)؛ Convolution neural network (CNN)؛ Pollution prediction؛ Gaussian Plume Model؛ Gases | ||
| مراجع | ||
|
Agrawal, A., & Mittal, N. (2020). Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Visual Computer, 36(2), 405–412. https://doi.org/10.1007/s00371-019-01630-9 Bagherzadeh, J., & Asil, H. (2019). A review of various semi-supervised learning models with a deep learning and memory approach. Iran Journal of Computer Science, 2(2), 65–80. https://doi.org/10.1007/s42044-018-00027-6 Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021). Air-pollution prediction in smart city, deep learning approach. Journal of Big Data, 8(1), 1–21. https://doi.org/10.1186/s40537-021-00548-1 Castiñeira, D., Schlosser, K. R., Geva, A., Rahmani, A. R., Fiore, G., Walsh, B. K., Smallwood, C. D., Arnold, J. H., & Santillana, M. (2020). Adding continuous vital sign information to static clinical data improves the prediction of length of stay after intubation: A data-driven machine learning approach. Respiratory Care, 65(9), 1367–1377. https://doi.org/10.4187/respcare.07561 Dargazany, A. R., Stegagno, P., & Mankodiya, K. (2018). WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics - Concept, Literature, and Future. Mobile Information Systems, 2018(Dl). https://doi.org/10.1155/2018/8125126 Davies, E. R. (2018). Face detection and recognition. In Computer Vision (Issue June). https://doi.org/10.1016/b978-0-12-809284-2.00021-6 Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An Introductory Review of Deep Learning for Prediction Models With Big Data. Frontiers in Artificial Intelligence, 3(February), 1–23. https://doi.org/10.3389/frai.2020.00004 Ferreira, P., Le, D. C., & Zincir-Heywood, N. (2019). Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection. 2019 15th International Conference on Network and Service Management (CNSM), (pp. 1-7). IEEE. Hayou, S., Doucet, A., & Rousseau, J. (2019). On the impact of the activation function on deep neural networks training. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 4746–4754. Heydari, A., Majidi Nezhad, M., Astiaso Garcia, D., Keynia, F., & De Santoli, L. (2022). Air pollution forecasting application based on deep learning model and optimization algorithm. Clean Technologies and Environmental Policy, 24(2), 607–621. https://doi.org/10.1007/s10098-021-02080-5 Isam Drewil, G., & Jabbar Al-Bahadili, R. (2021). Multicultural Education Forecast Air Pollution in Smart City Using Deep Learning Techniques: A Review. Multicultural Education, 7(5), 38–47. https://doi.org/10.5281/zenodo.4737746 Khan, Z. Y., Niu, Z., Sandiwarno, S., & Prince, R. (2021). Deep learning techniques for rating prediction: a survey of the state-of-the-art. In Artificial Intelligence Review (Vol. 54, Issue 1). Springer Netherlands. https://doi.org/10.1007/s10462-020-09892-9 Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017). Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environmental Pollution, 231, 997–1004. https://doi.org/10.1016/j.envpol.2017.08.114 Ma, M., Dong, Z., Jang, C., Zhu, Y., & Zheng, H. (2020). Deep learning for prediction of the air quality response to emission changes. Environmental Science & Technology, 54(14), 8589–8600. https://doi.org/10.1021/acs.est.0c02923.Deep Mairal, J. (2016). End-to-end kernel learning with supervised convolutional kernel networks. Advances in Neural Information Processing Systems, Nips, 1407–1415. Mao, Y., & Lee, S. (2019). Deep Convolutional Neural Network for Air Quality Prediction. Journal of Physics: Conference Series, 1302(3), 032046. https://doi.org/10.1088/1742-6596/1302/3/032046 Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68–71. https://doi.org/10.1016/j.fcij.2017.12.001 Mureşan, H., & Oltean, M. (2018). Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica, 10(1), 26–42. https://doi.org/10.2478/ausi-2018-0002 Muthukumar, P., Cocom, E., Nagrecha, K., Comer, D., Burga, I., Taub, J., Calvert, C. F., Holm, J., & Pourhomayoun, M. (2022). Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data. Air Quality, Atmosphere and Health, 15(7), 1221–1234. https://doi.org/10.1007/s11869-021-01126-3 Oprea, M., Popescu, M., Mihalache, S. F., & Dragomir, E. G. (2017). Data mining and ANFIS application to particulate matter air pollutant prediction. A comparative study. ICAART 2017 - Proceedings of the 9th International Conference on Agents and Artificial Intelligence, 2(Icaart), 551–558. https://doi.org/10.5220/0006196405510558 Pires, I. M., Hussain, F., Garcia, N. M., Lameski, P., & Zdravevski, E. (2020). Homogeneous data normalization and deep learning: A case study in human activity classification. Future Internet, 12(11), 1–14. https://doi.org/10.3390/fi12110194 Schürholz, D., Kubler, S., & Zaslavsky, A. (2020). Artificial intelligence-enabled context-aware air quality prediction for smart cities. Journal of Cleaner Production, 271, 121941. https://doi.org/10.1016/j.jclepro.2020.121941 Towfek El-Kenawy, E.-S. M. (2019). A Machine Learning Model for Hemoglobin Estimation and Anemia Classification. International Journal of Computer Science and Information Security (IJCSIS), 17(2). https://sites.google.com/site/ijcsis/ Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., Lozupone, C., Zaneveld, J. R., Vázquez-Baeza, Y., Birmingham, A., Hyde, E. R., & Knight, R. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome, 5(1), 1–18. https://doi.org/10.1186/s40168-017-0237-y Wu, W., May, R. J., Maier, H. R., & Dandy, G. C. (2013). A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks. Water Resources Research, 49(11), 7598–7614. https://doi.org/10.1002/2012WR012713 Zhang, J., & Ding, W. (2017). Prediction of air pollutants concentration based on an extreme learning machine: The case of Hong Kong. International Journal of Environmental Research and Public Health, 14(2), 1–19. https://doi.org/10.3390/ijerph14020114 Zhang, Q., Lam, J. C., Li, V. O., & Han, Y. (2020). Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast. ArXiv Preprint ArXiv:2001.11957, 1–7. http://arxiv.org/abs/2001.11957 Zhong, Y., & Zhao, M. (2020). Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168(August 2019), 105146. https://doi.org/10.1016/j.compag.2019.105146 Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A machine learning approach for air quality prediction: Model regularization and optimization. Big Data and Cognitive Computing, 2(1), 1–15. https://doi.org/10.3390/bdcc2010005 | ||
|
آمار تعداد مشاهده مقاله: 240 تعداد دریافت فایل اصل مقاله: 579 |
||