Applying Machine Learning Techniques in Air Quality Prediction—A Bucharest City Case Study

Grigore Cican (), Adrian-Nicolae Buturache and Radu Mirea
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Grigore Cican: Faculty of Aerospace Engineering, Polytechnic University of Bucharest, 1-7 Polizu Street, 1, 011061 Bucharest, Romania
Adrian-Nicolae Buturache: FasterEdu.com, 075100 Otopeni, Romania
Radu Mirea: National Research and Development Institute for Gas Turbines COMOTI, 220D Iuliu Maniu, 061126 Bucharest, Romania

Sustainability, 2023, vol. 15, issue 11, 1-20

Abstract: Air quality forecasting is very difficult to achieve in metropolitan areas due to: pollutants emission dynamics, high population density and uncertainty in defining meteorological conditions. The use of data, which contain insufficient information within the model training, and the poor selection of the model to be used limits the air quality prediction accuracy. In this study, the prediction of NO 2 concentration is made for the year 2022 using a long short-term memory network (LSTM) and a gated recurrent unit (GRU). this is an improvement in terms of performance compared to traditional methods. Data used for predictive modeling are obtained from the National Air Quality Monitoring Network. The KPIs(key performance indicator) are computed based on the testing data subset when the NO 2 predicted values are compared to the real known values. Further, two additional predictions were performed for two days outside the modeling dataset. The quality of the data is not as expected, and so, before building the models, the missing data had to be imputed. LSTM and GRU performance in predicting NO 2 levels is similar and reasonable with respect to the case study. In terms of pure generalization capabilities, both LSTM and GRU have the maximum R 2 value below 0.8. LSTM and GRU represent powerful architectures for time-series prediction. Both are highly configurable, so the probability of identifying the best suited solution for the studied problem is consequently high.

Keywords: machine learning; air quality; LSTM; GRU; NO 2; Bucharest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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