IDEAS home Printed from https://ideas.repec.org/a/aes/dbjour/v13y2022i1p1-10.html
   My bibliography  Save this article

Analysis of Romanian Air Quality using Machine Learning Techniques

Author

Listed:
  • Andreea-Mihaela NICULAE

    (The Bucharest University of Economic Studies, Romania)

Abstract

Air quality monitoring has become an increasingly important subject and is one of the most important concerns of governments worldwide. Monitoring is especially important in industrial and urban areas. Due to the many forms of pollution generated mainly by fuel consumption, means of transport, coal-fired electricity generation, etc., air quality is negatively affected. As the current trend is an increase in air pollution, it is necessary to install equipment to measure air quality both in areas with a high risk of pollution and in areas where pollution is low. These types of equipment must communicate in real-time their measured values, which then can be accessed to be able to make analyzes and predictions regarding air quality in a certain geographical area, areas with a high industrialization level, or in areas with a growing population. This paper aims to investigate the application of big data and machine learning techniques to make predictions on air quality using, as a source of data, data recorded in the period 2018-2021 from measurement probes throughout Romania for PM10, NO2, O3, and SO2. The results of this paper's analysis show that time-series models outperform traditional models. Moreover, ANN models are successful only in classifying pollutants' AQI levels and not their actual values.

Suggested Citation

  • Andreea-Mihaela NICULAE, 2022. "Analysis of Romanian Air Quality using Machine Learning Techniques," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 13(1), pages 1-10.
    • Handle: RePEc:aes:dbjour:v:13:y:2022:i:1:p:1-10
    as

    Download full text from publisher

    File URL: https://dbjournal.ro/archive/33/33_1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    2. Duan, Yunlong & Mu, Chang & Yang, Meng & Deng, Zhiqing & Chin, Tachia & Zhou, Li & Fang, Qifeng, 2021. "Study on early warnings of strategic risk during the process of firms’ sustainable innovation based on an optimized genetic BP neural networks model: Evidence from Chinese manufacturing firms," International Journal of Production Economics, Elsevier, vol. 242(C).
    3. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
    4. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
    5. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    6. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
    7. Berthine Nyunga Mpinda & Jules Sadefo-Kamdem & Salomey Osei & Jeremiah Fadugba, 2021. "Accuracies of Model Risks in Finance using Machine Learning," Working Papers hal-03191437, HAL.
    8. Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
    9. Ritika Chopra & Gagan Deep Sharma, 2021. "Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    10. Paraskevas Panagiotidis & Andrew Effraimis & George A Xydis, 2019. "An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids," Energy & Environment, , vol. 30(1), pages 63-80, February.
    11. Meftah Elsaraiti & Adel Merabet, 2021. "A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed," Energies, MDPI, vol. 14(20), pages 1-16, October.
    12. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    13. Nwokike Chukwudike C. & Ugoala & Chukwuma B. & Obubu Maxwell & Uche-Ikonne Okezie O. & Offorha Bright C. & Ukomah Henry I., 2020. "Forecasting Monthly Prices of Gold Using Artificial Neural Network," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(3), pages 1-2.
    14. Yoojeong Song & Jae Won Lee & Jongwoo Lee, 2022. "Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 27-38, January.
    15. Phillips Edomwonyi Obasohan, 2020. "Comparing Weighted Markov Chain and Auto-Regressive Integrated Moving Average in the Prediction of Under-5 Mortality Annual Closing Rates in Nigeria," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(3), pages 1-13, May.
    16. Sehrish Kayani & Usman Ayub & Imran Abbas Jadoon, 2019. "Adaptive Market Hypothesis and Artificial Neural Networks: Evidence from Pakistan," Global Regional Review, Humanity Only, vol. 4(2), pages 190-203, June.
    17. Gourav Kumar & Uday Pratap Singh & Sanjeev Jain, 2022. "Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 991-1039, October.
    18. Chaoran Cui & Xiaojie Li & Juan Du & Chunyun Zhang & Xiushan Nie & Meng Wang & Yilong Yin, 2021. "Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction," Papers 2107.14033, arXiv.org, revised Mar 2022.
    19. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    20. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:dbjour:v:13:y:2022:i:1:p:1-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adela Bara (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.