Comparative analysis of machine learning algorithms based on an air pollution prediction model

• Autorzy: Wiktorzak Aneta , Kaczorowski B.

Identyfikatory

DOI

10.34808/dp3a-5n10

• Języki publikacji: EN

Abstrakty

EN

In this paper it has been assumed that the use of artificial intelligence algorithms to predict the level of air quality gives good results. Our goal was to perform a comparative analysis of machine learning algorithms based on an air pollution prediction model. By repeatedly performing tests on a number of models, it was possible to establish both the positive and negative influence of the parameters on the result generated by the ANN model. The research was based on some selected both current and historical data of the air pollution concentration altitude and weather data. The research was carried out with the help of the Python 3 programming language, along with the necessary libraries such as TensorFlow and Jupyter Notebook. The analysis of the results showed that the optimal solution was to use the Long Stort Term Memory LSTM algorithm in smog prediction. It is a recursive model of an artificial neural network that is ideally suited for prediction tasks. Further research on the models may develop in various directions, ranging from increasing the number of trials which would be linked to more reliable data, ending with increasing the number of types of algorithms studied. Developing the models by testing other types of activation and optimization functions would also be able to improve the understanding of how they affect the data presented. A very interesting developmental task may be to focus on a self-learning artificial intelligence algorithm, so that the algorithm can learn on a regular basis, not only on historical data. These studies would contribute significantly to the amount of data collected, its analysis and prediction quality in the future.

Słowa kluczowe

EN

ANN; Python; LSTM

PL

ANN; Python; LSTM

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2022
• Tom: Vol. 26, No 4
• Strony: 1--10
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Wiktorzak Aneta

• Lomza State University of Applied Sciences

• https://orcid.org/0000-0002-3885-1340

autor

Kaczorowski B.

• Lomza State University of Applied Sciences

Bibliografia

• [1] P. Kleczkowski, Smog w Polsce. Warszawa: Wydawnictwo naukowe PWN, 2020.
• [2] J. Chełmiński, SMOG. Diesle, kopciuchy, kominy, czyli dlaczego w Polsce nie da się oddychać? Poznań: Wydawnictwo Poznańskie, 2019.
• [3] H. Mazurek and A. Badyda, Smog. Konsekwencje zdrowotne zanieczyszczeń powietrza. Warszawa: PZWL Wydawnictwo Lekarskie, 2021.
• [4] K. Górka, Ocena skuteczności polityki antysmogowej Państwa. Wrocław: Prace naukowe uniwersytetu ekonomicznego we Wrocławiu, 2018.
• [5] Airly, “Airly.” https://airly.org, 2022. dostęp: 16.05.2022.
• [6] GIOŚ, “GioŚ.” https://www.gios.gov.pl/pl/, 2022. dostęp: 16.05.2022.
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• [8] E. P. GIG, “Eko patrol gig.” https://monitoringjakoscipowietrza.pl/, 2022. dostęp: 16.05.2022.
• [9] InConTech, “Incontech.” http://incontech.eu/, 2022. dostęp: 16.05.2022.
• [10] M. Kasperski, Sztuczna inteligencja. Warszawa: Wydawnictwo Helion, 2003.
• [11] S. Raschaka and V. Mirjalili, Python. Machine learning i deep learning. Gliwice: Wydawnictwo Helion, 2021. Biblioteki scikitlearn i TensorFlow 2.
• [12] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Systemy uczące się. Warszawa: Wydawnictwo PWN, 2018.
• [13] Z. Valentino, Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe. Gliwice: Wydawnictwo Helion, 2021.
• [14] D. Qin, J. Yu, G. Zou, R. Yong, Q. Zhao, and B. Zhang, “A novel combined prediction scheme based on cnn and lstm for urban pm2.5 concentration,” IEEE Access, vol. 7, pp. 172052–172061, 2019.
• [15] T.-C. Bui, V.-D. Le, and S.-K. Cha, “A deep learning approach for forecasting air pollution in south korea using lstm,” arXiv, p. 1801.05746, 2018.
• [16] M. Zeinalnezhad, A. Gholamzadeh, and J. Klemeš, “Air pollution prediction using semi-experimental regression model and adaptive neuro-fuzzy inference system,” Journal of Cleaner Production, vol. 261, p. 121218, 2020.
• [17] 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 with Applications, vol. 160, p. 113726, 2020.
• [18] A. Heydari, M. Majidi Nezhad, D. Astiaso Garcia, A. H. Gandomi, H. Karami, and A. H. Alavi, “Air pollution forecasting application based on deep learning model and optimization algorithm,” Clean Technologies and Environmental Policy, vol. 24, p. 607–621, 2022.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).