Abstract
The aim of this paper is to calculate the Air Quality Index (AQI) for each pollutant during a single year for Canton Sarajevo. After obtaining the Air Quality Index for all pollutants during an observed year, the calculation of the total Air Quality Index is given. Based on the collected hourly measured values of pollution concentrations for the period from 2014 to 2018 using the hybrid EMD-SARIMA model, the values of the Air Quality Index for 2019 are forecasted. After obtaining the prediction results for the model, four different measures were taken to identify the performance of the model. The Mean Absolute Percentage Error MAPE of 6.66 (%) shows highly accurate model performance forecasts. The model is created and performance of the proposed model has been tested in the MATLAB.
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References
Information on the state of air quality in the Sarajevo Canton for 2018. Ministry of Physical Planning, Construction and Environmental Protection. mpz@mpz.ks.gov.ba
Air quality in Sarajevo Canton—website of the Ministry of Physical Planning. Construction and environmental protection KS. www.kvalitetzraka.ba
Riddle, A., Carruthers, D., Sharpe, A., McHugh, C., Stocker, J.: Comparisons between FLUENT and ADMS for atmospheric dispersion modelling. Atmos. Environ. 38, 1029–1038 (2004)
Rahman, N.H.A., Lee, M.H., Suhartono, Latif, M.T. Artificial neural networks and fuzzy time series forecasting: an application to air quality. Qual. Quant. 49, 2633–2647 (2015)
Grivas, G., Chaloulakou, A.: Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens. Greece. Atmos. Environ. 40, 1216–1229 (2006)
Elangasinghe, M.A., Singhal, N., Dirks, K.N., Salmond, J.A.: Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos. Pollut. Res. 5, 696–708 (2014)
Bai, Y., Li, Y., Wang, X., Xie, J., Li, C.: Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos. Pollut. Res. 7, 557–566 (2016)
Mishra, D., Goyal, P.: NO2 forecasting models Agra. Atmos. Pollut. Res. 6, 99–106 (2015)
Kurt, A., Oktay, A.B.: Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Syst. Appl. 37, 7986–7992 (2010)
Song, Y., Qin, S., Qu, J., Liu, F.: The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region. Atmos. Environ. 118, 58–69 (2015)
Silibello, C., D’Allura, A., Finardi, S., Bolignano, A., Sozzi, R.: Application of bias adjustment techniques to improve air quality forecasts. Atmos. Pollut. Res. 6, 928–938 (2015)
Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Upper Saddle River (1992)
Qin, S., Liu, F., Wang, J., Sun, B.: Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmos. Environ. 98, 665–675 (2014)
Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. Adv. Neural Inf. Process. Syst. 9, 155–161 (1997)
Cortina-Januchs, M.G., Quintanilla-Dominguez, J., Vega-Corona, A., Andina, D.: Development of a model for forecasting of PM10 concentrations in Salamanca. Mexico. Atmos. Pollut. Res. 6, 626–634 (2015)
Technical Assistance Document for the Reporting of Daily Air Quality—the Air Quality Index; U.S. Environmental Protection Agency (2016)
https://www.fhmzbih.gov.ba/latinica/ZRAK/AQI-metodologija.php
Nai, W., Liu, Lu., Wang, S., Dong, D.: An EMD-SARIMA-based modeling approach for air traffic forecasting. Algorithms 10, 139 (2017). https://doi.org/10.3390/a10040139
Dedović, M.M., Avdaković, S.: A new approach for df/dt and active power imbalance in power system estimation using Huang’s empirical mode decomposition. Int. J. Electr. Power Energy Syst. 110, 62–71 (2019)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. London A 454(1971), 903–995 (1998)
Bai, L., Wang, J., Ma, X., Lu, H.: Air Pollution forecasts: an overview. Int. J. Environ. Res. Publ. Health 15, 780 (2018)
Bošnjak, R. (2019). Forecast of time series using Scikit-learn software libraries. Graduation Thesis. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:765650
Baggio, R., Klobas, J.: Quantitative methods in tourism. Channel View Publication, Bristol-Buffalo-Toronto (2011)
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Dedovic, M.M., Avdaković, S., Mujezinović, A., Dautbasic, N. (2021). The Hybrid EMD-SARIMA Model for Air Quality Index Prediction, Case of Canton Sarajevo. In: Avdaković, S., Volić, I., Mujčić, A., Uzunović, T., Mujezinović, A. (eds) Advanced Technologies, Systems, and Applications V. IAT 2020. Lecture Notes in Networks and Systems, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-030-54765-3_9
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