A hybrid model for predicting air quality combining Holt–Winters and Deep Learning Approaches: A novel method to identify ozone concentration peaks
| dc.citation.epage | 1163 | |
| dc.citation.issue | 4 | |
| dc.citation.journalTitle | Математичне моделювання та комп'ютинг | |
| dc.citation.spage | 1154 | |
| dc.contributor.affiliation | Університет Абдельмалека Ессааді | |
| dc.contributor.affiliation | Головне управління метеорології | |
| dc.contributor.affiliation | Abdelmalek Essaadi University | |
| dc.contributor.affiliation | General Directorate of Meteorology | |
| dc.contributor.author | Марракчі, Н. | |
| dc.contributor.author | Бергам, А. | |
| dc.contributor.author | Фахурі, Н. | |
| dc.contributor.author | Кенза, К. | |
| dc.contributor.author | Marrakchi, N. | |
| dc.contributor.author | Bergam, A. | |
| dc.contributor.author | Fakhouri, H. | |
| dc.contributor.author | Kenza, K. | |
| dc.coverage.placename | Львів | |
| dc.date.accessioned | 2025-03-10T09:21:54Z | |
| dc.date.created | 2023-02-28 | |
| dc.date.issued | 2023-02-28 | |
| dc.description.abstract | Озон (O3) з тропосфери є однією з речовин, яка сильно впливає на забруднення повітря в місті Танжер. Прогнозування цього забруднювача може покращити якість повітря. У цій статті представлено новий підхід, який поєднує алгоритми глибинного навчання та метод Хольта–Вінтерса для виявлення піків забруднюючих речовин і отримання більш точної моделі прогнозування. З огляду на те, що LSTM є надзвичайно потужним алгоритмом, ми об’єднали його з методом Хольта–Вінтерса, щоб покращити модель. Використовуючи декілька показників точності, досліджено ефективність моделей. Емпіричні результати показують перевагу гібридної моделі, надаючи більш точні прогнози з індексом згоди, що дорівнює 0.91. | |
| dc.description.abstract | Ozone (O3) from the troposphere is one of the substances that has a strong effect on air pollution in the city of Tanger. Prediction of this pollutant can have positive improvements in air quality. This paper presents a new approach combining deep-learning algorithms and the Holt–Winters method in order to detect pollutant peaks and obtain a more accurate forecasting model. Given that LSTM is an extremely powerful algorithm, we hybridized with the Holt–Winters method to enhance the model. Making use of multiple accuracy metrics, the models' efficiency is investigated. Empirical findings reveal the superiority of the hybrid model by providing forecasts that are more accurate with an index of agreement equal to 0.91. | |
| dc.format.extent | 1154-1163 | |
| dc.format.pages | 10 | |
| dc.identifier.citation | A hybrid model for predicting air quality combining Holt–Winters and Deep Learning Approaches: A novel method to identify ozone concentration peaks / N. Marrakchi, A. Bergam, H. Fakhouri, K. Kenza // Mathematical Modeling and Computing. — Lviv Politechnic Publishing House, 2023. — Vol 10. — No 4. — P. 1154–1163. | |
| dc.identifier.citationen | A hybrid model for predicting air quality combining Holt–Winters and Deep Learning Approaches: A novel method to identify ozone concentration peaks / N. Marrakchi, A. Bergam, H. Fakhouri, K. Kenza // Mathematical Modeling and Computing. — Lviv Politechnic Publishing House, 2023. — Vol 10. — No 4. — P. 1154–1163. | |
| dc.identifier.doi | doi.org/doi.org/10.23939/mmc2023.04.1154 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/64067 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Математичне моделювання та комп'ютинг, 4 (10), 2023 | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 4 (10), 2023 | |
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| dc.relation.references | [23] Willmott C. J., Robeson S. M., Matsuura K. A refined index of model performance. International Journal of Climatology. 32 (13), 2088–2094 (2012). | |
| dc.relation.referencesen | [1] Samadi A., Achelhi H. Industry 4.0 in The Economic Activity Zones in Morocco: Tangier-TetouanAlhoceima Region Case. International Journal of Accounting, Finance, Auditing, Management and Economics. 2 (6-1), 327–338 (2021). | |
| dc.relation.referencesen | [2] Zhang B., Song C., Li Y., Jiang X. Spatiotemporal prediction of O3 concentration based on the KNNProphet-LSTM model. Heliyon. 8 (11), e11670 (2022). | |
| dc.relation.referencesen | [3] Lim C. C., Hayes R. B., Ahn J., Shao Y., Silverman D. T., Jones R. R., Garcia C., Bell M. L., Thurston G. D. Long-term exposure to ozone and cause-specific mortality risk in the united states. American Journal of Respiratory and Critical Care Medicine. 200 (8), 1022–1031 (2019). | |
| dc.relation.referencesen | [4] Suraboyina S., Allu S. K., Anupoju G. R., Polumati A. A comparative predictive analysis of backpropagation artificial neural networks and non-linear regression models in forecasting seasonal ozone concentrations. Journal of Earth System Science. 131 (3), 189 (2022). | |
| dc.relation.referencesen | [5] Kovaˇc-Andri´c E., Sheta A., Faris H., Gajdoˇsik M. S. Forecasting ozone concentrations in the east of Croatia ˇ using nonparametric Neural Network Models. Journal of Earth System Science. 125, 997–1006 (2016). | |
| dc.relation.referencesen | [6] Ensafi Y., Amin S. H., Zhang G., Shah B. Time-series forecasting of seasonal items sales using machine learning–a comparative analysis. International Journal of Information Management Data Insights. 2 (1), 100058 (2022). | |
| dc.relation.referencesen | [7] Chattopadhyay G., Chattopadhyay S. Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach. Computers & Geosciences. 35 (9), 1925–1932 (2009). | |
| dc.relation.referencesen | [8] Akbarzadeh A., Vesali Naseh M., NodeFarahani M. Carbon monoxide prediction in the atmosphere of tehran using developed support vector machine. Pollution. 6 (1), 43–57 (2020). | |
| dc.relation.referencesen | [9] Kaur J., Parmar K. S., Singh S. Autoregressive models in environmental forecasting time series: a theoretical and application review. Environmental Science and Pollution Research. 30, 19617–19641 (2023). | |
| dc.relation.referencesen | [10] Oufdou H., Bellanger L., Bergam A., El Ghaziri A., Khomsi K., Qannari E. M., et al. Comparison of Different Regularized and Shrinkage Regression Methods to Predict Daily Tropospheric Ozone Concentration in the Grand Casablanca Area. Advances in Pure Mathematics. 8 (10), 793 (2018). | |
| dc.relation.referencesen | [11] Hong F., Ji C., Rao J., Chen C., Sun W. Hourly ozone level prediction based on the characterization of its periodic behavior via deep learning. Process Safety and Environmental Protection. 174, 28–38 (2023). | |
| dc.relation.referencesen | [12] Tsai C.-h., Chang L.-c., Chiang H.-c. Forecasting of ozone episode days by cost-sensitive neural network methods. Science of the Total Environment. 407 (6), 2124–2135 (2009). | |
| dc.relation.referencesen | [13] Tamas W. W., Notton G., Paoli C., Nivet M.-L., Voyant C. Hybridization of air quality forecasting models using machine learning and clustering: An original approach to detect pollutant peaks. Aerosol and Air Quality Research. 16 (2), 405–416 (2016). | |
| dc.relation.referencesen | [14] Belavadi S. V., Rajagopal S., Ranjani R., Mohan R. Air quality forecasting using LSTM RNN and wireless sensor networks. Procedia Computer Science. 170, 241–248 (2020). | |
| dc.relation.referencesen | [15] Cinar Y. G., Mirisaee H., Goswami P., Gaussier E., A¨ıt-Bachir A. Period-aware content attention RNNs for time series forecasting with missing values. Neurocomputing. 312, 177–186 (2018). | |
| dc.relation.referencesen | [16] Braik M., Sheta A., Al-Hiary H. Hybrid neural network models for forecasting ozone and particulate matter concentrations in the Republic of China. Air Quality, Atmosphere & Health. 13, 839–851 (2020). | |
| dc.relation.referencesen | [17] Jamei M., Ali M., Malik A., Karbasi M., Sharma E., Yaseen Z. M. Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model. Journal of Cleaner Production. 374, 134011 (2022). | |
| dc.relation.referencesen | [18] Maia A. L. S., de Carvalho F. D. A. T. Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. International Journal of Forecasting. 27 (3), 740–759 (2011). | |
| dc.relation.referencesen | [19] Dantas T. M., Oliveira F. L. C., Repolho H. M. V. Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management. 59, 116–123 (2017). | |
| dc.relation.referencesen | [20] Dullah H., Ahmed A. N., Kumar P., Elshafie A. Integrated nonlinear autoregressive neural network and Holt Winters exponential smoothing for river streaming flow forecasting at Aswan High. Earth Science Informatics. 16 (1), 773–786 (2023). | |
| dc.relation.referencesen | [21] Hyndman R., Koehler A. B., Ord J. K., Snyder R. D. Forecasting with Exponential Smoothing: The State Space Approach. Springer Science & Business Media (2008). | |
| dc.relation.referencesen | [22] Programmer L. Deep Learning: Recurrent Neural Networks in Python, LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) (2016). | |
| dc.relation.referencesen | [23] Willmott C. J., Robeson S. M., Matsuura K. A refined index of model performance. International Journal of Climatology. 32 (13), 2088–2094 (2012). | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2023 | |
| dc.subject | прогнозування якості повітря | |
| dc.subject | озон (O3) | |
| dc.subject | довга короткочасна пам’ять (LSTM) | |
| dc.subject | метод Хольта–Вінтерса | |
| dc.subject | рекурентна нейронна мережа (RNN) | |
| dc.subject | штучні нейронні мережі | |
| dc.subject | Air quality forecasting | |
| dc.subject | Ozone (O3) | |
| dc.subject | Long Short-Term Memory (LSTM) | |
| dc.subject | Holt–Winters method | |
| dc.subject | Recurrent Neural Network (RNN) | |
| dc.subject | Artificial Neural Networks | |
| dc.title | A hybrid model for predicting air quality combining Holt–Winters and Deep Learning Approaches: A novel method to identify ozone concentration peaks | |
| dc.title.alternative | Гібридна модель для прогнозування якості повітря, що поєднує підходи Хольта–Вінтерса та глибинного навчання: новий метод визначення піків концентрації озону | |
| dc.type | Article |
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