Multi-layer long short-term memory (LSTM) prediction model on air pollution for Konya province

Year 2022, , 93 - 100, 31.12.2022

Yahya Koçak , Murat Koklu

https://doi.org/10.18100/ijamec.1208256

Cited By: 1

Abstract

One of the main problems of the developing and changing world is air pollution. In addition to human causes such as population growth, increase in the number of vehicles producing exhaust emissions in line with the population, development of industry, natural causes such as forest fires, volcano eruptions and dust storms also play a role in increasing air pollution. Air pollution has become a bigger problem that reduces the quality of life of living beings and causes various lung and heart diseases due to reasons such as the growing proximity of settlements to industrial zones due to population growth, the increase in the number of individual vehicles, and zoning works carried out by ignoring air quality. Both international organizations and local authorities take various measures to control and prevent air pollution. In Turkey, necessary legal arrangements have been made within the scope of these measures and air quality monitoring stations have been established. The task of these stations is to measure pollutants such as PM10, CO, SO2 together with meteorological data such as air temperature, humidity, wind speed and direction. In this study, a prediction model for the future concentrations of PM10, CO and SO2 pollutants using the measurement data from three different air quality monitoring stations in Konya between January 2020 and January 2021 was realized with a multi-layer Long Short Term Memory (LSTM) artificial neural network. The Root Mean Square Deviation (RMSE) and Mean Absolute Percentage Error (MAPE) methods was used to calculate the performance of the study. As a result of the study, it is observed that the multi-layer LSTM architecture is more successful than the single-layer architecture.

Keywords

Air pollution, Air quality, Artificial neural network, Lstm, Multi-layer, Prediction

References

• W. Nazar and M. Niedoszytko, “Air Pollution in Poland: A 2022 Narrative Review with Focus on Respiratory Diseases.” International journal of environmental research and public health vol. 19,2 895. 14 Jan. 2022, doi:10.3390/ijerph19020895
• World Health Organization, “Ambient (Outdoor) Air Pollution” 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health [Accessed: 15-October-2022].
• M. Koklu, R. Kursun, Y. S. Taspinar and I. Cinar (2021). Classification of date fruits into genetic varieties using image analysis. Mathematical Problems in Engineering, 2021.
• World Health Organization. Regional Office for Europe. (‎2000)‎. Air quality guidelines for Europe, 2nd ed.. World Health Organization. Regional Office for Europe.
• M. Kolehmainen, H. Martikainen, J. Ruuskanen, Neural networks and periodic components used in air quality forecasting, Atmospheric Environment, 35,5 815-825, 2001, ISSN 1352-2310, https://doi.org/10.1016/S1352-2310(00)00385-X.
• Y. Unal, Y. S. Taspinar, I. Cinar, R. Kursun and M. Koklu (2022). Application of pre-trained deep convolutional neural networks for coffee beans species detection. Food Analytical Methods, 15(12), 3232-3243.
• H. Maleki et al. Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy. 2019 Aug;21(6):1341-1352. DOI: 10.1007/s10098-019-01709-w. PMID: 33907544; PMCID: PMC8075317.
• S. Xu et al. A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks. Sci Rep 12, 14434 (2022). https://doi.org/10.1038/s41598-022-17754-3.
• M. Koklu, M. F. Unlersen, I. A. Ozkan, M. Fatih Aslan, K. Sabanci, A CNN-SVM study based on selected deep features for grapevine leaves classification, Measurement, Volume 188, 2022, 110425, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.110425.
• Q. Zhang, Y. Han, V. O. K. Li and J. C. K. Lam, "Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities," in IEEE Access, vol. 10, pp. 55818-55841, 2022, doi: 10.1109/ACCESS.2022.3174853.
• Z. Qingping, J. Haiyan, W. Jianzhou, Z. Jianling, A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network, Science of The Total Environment, Volume 496, 2014, Pages 264-274, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2014.07.051.
• M.A. Elangasinghe, N. Singhal, K.N. Dirks, J.A. Salmond, S. Samarasinghe, Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering, Atmospheric Environment, Volume 94, 2014, Pages 106-116, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2014.04.051.
• M. Koklu, H. Kahramanli, N. Allahverdi, Applications of Rule Based Classification Techniques for Thoracic Surgery, Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society: Managing Intellectual Capital and Innovation; Proceedings of the MakeLearn and TIIM Joint International Conference 2, 2015.
• H. Xuefei et al., Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model, Remote Sensing of Environment, Volume 140, 2014, Pages 220-232, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2013.08.032.
• F. Deng, L. Ma, X. Gao and J. Chen, "The MR-CA Models for Analysis of Pollution Sources and Prediction of PM2.5," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 4, pp. 814-820, April 2019, doi: 10.1109/TSMC.2017.2721100.
• D. Maharani, Murfi H., “Deep Neural Network For Structured Data - A Case Study Of Mortality Rate Prediction Caused By Air Quality”, Journal of Physics: Conference Series, 1192, 012010, 2019, doi: 10.1088/1742-6596/1192/1/012010.
• M. Koklu, I. Cinar, and Y. S. Taspinar (2022). CNN-based bi-directional and directional long-short term memory network for determination of face mask. Biomedical Signal Processing and Control, 71, 103216.
• J. Wang, G. Song, A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction, Neurocomputing, Volume 314, 2018, Pages 198-206, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2018.06.049.
• M. F. Aslan, M. F. Unlersen, K. Sabanci, and A. Durdu (2021). CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Applied Soft Computing, 98, 106912.
• P. -W. Soh, J. -W. Chang and J. -W. Huang, "Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations," in IEEE Access, vol. 6, pp. 38186-38199, 2018, doi: 10.1109/ACCESS.2018.2849820.
• K. Cho, B. -Y. Lee, M. Kwon and S. Kim. (2019). Air Quality Prediction Using a Deep Neural Network Model. Journal of Korean Society for Atmospheric Environment, 35(2), 214-225.
• Y. S. Taspinar, M. Dogan, I. Cinar, R. Kursun, I. A. Ozkan, and M. Koklu (2022). Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques. European Food Research and Technology, 248(11), 2707-2725.
• M. R. Delavar, A. Gholami, G. R. Shiran, Y. Rashidi, G.R. Nakhaeizadeh, K. Fedra, S. H. Afshar, A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. ISPRS Int. J. Geo-Inf. 2019, 8, 99. https://doi.org/10.3390/ijgi8020099.
• Y. Rybarczyk, R. Zalakeviciute. Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review. Applied Sciences. 2018; 8(12):2570. https://doi.org/10.3390/app8122570.
• M. F. Unlersen, S. Balci, M. F. Aslan and K. Sabanci (2022). The speed estimation via BiLSTM-based network of a BLDC motor drive for fan applications. Arabian Journal for Science and Engineering, 47(3), 2639-2648.
• F. Franceschi, M. Cobo, M. Figueredo, Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering, Atmospheric Pollution Research, Volume 9, Issue 5, 2018, Pages 912-922, ISSN 1309-1042, https://doi.org/10.1016/j.apr.2018.02.006.
• M. Koklu, H. Kahramanli, N. Allahverdi (2012), A New Approach to Classification Rule Extraction Problem by the Real Value Coding, International Journal of Innovative Computing, Information and Control, 8(9), pp.6303-6315.
• S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
• M. Koklu, H. Kahramanli, N. Allahverdi, (2014), A new accurate and effıcıent approach to extract classification rules, Journal of the Faculty of Engineering and Architecture of Gazi University, 29(3), pp.477-486, Doi: 17341/gummfd.89433.
• Y. -S. Chang, H. -T. Chiao, S. Abimannan, Y. -P. Huang, Y. -T. Tsai, K. -M. Lin, An LSTM-based aggregated model for air pollution forecasting, Atmospheric Pollution Research, Volume 11, Issue 8, 2020, Pages 1451-1463, ISSN 1309-1042, https://doi.org/10.1016/j.apr.2020.05.015.
• D. Singh, Y. S. Taspinar, R. Kursun, I. Cinar, M. Koklu, I. A. Ozkan and H. N. Lee (2022). Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models. Electronics, 11(7), 981.
• N. A. B. Mabahwi, O. L. H. Leh, D. Omar, Human Health and Wellbeing: Human Health Effect of Air Pollution, Procedia - Social and Behavioral Sciences, Volume 153, 2014, Pages 221-229, ISSN 1877-0428, https://doi.org/10.1016/j.sbspro.2014.10.056.
• J.O. Anderson, J.G. Thundiyil & A. Stolbach, Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health. J. Med. Toxicol. 8, 166–175 (2012). https://doi.org/10.1007/s13181-011-0203-1.
• A. Ghorani-Azam, B. Riahi-Zanjani, M. Balali-Mood, Effects of air pollution on human health and practical measures for prevention in Iran. J Res Med Sci. 2016 Sep 1;21:65. doi: 10.4103/1735-1995.189646. PMID: 27904610; PMCID: PMC5122104.
• E. Tagaris, K.-J. Liao, A. J. DeLucia, L. Deck, P. Amar, and A. G. Russell, Environmental Science & Technology 2009 43 (13), 4979-4988 DOI: 10.1021/es803650w.
• M. Krzyzanowski, A. Cohen, Update of WHO air quality guidelines. Air Qual Atmos Health 1, 7–13 (2008). https://doi.org/10.1007/s11869-008-0008-9.
• World Health Organization. Regional Office for Europe. (‎2000)‎. Evolution of WHO air quality guidelines: past, present and future, World Health Organization. Regional Office for Europe.
• Y. Yu, X. Si, C. Hu and J. Zhang, A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural Computation, vol. 31, no. 7, pp. 1235-1270, July 2019, doi: 10.1162/neco_a_01199.
• K. Smagulova, A.P. James, A survey on LSTM memristive neural network architectures and applications. Eur. Phys. J. Spec. Top. 228, 2313–2324 (2019). https://doi.org/10.1140/epjst/e2019-900046-x.
• R. DiPietro, G. D. Hager, Chapter 21 - Deep learning: RNNs and LSTM, Editor(s): S. Kevin Zhou, Daniel Rueckert, Gabor Fichtinger, In The Elsevier and MICCAI Society Book Series, Handbook of Medical Image Computing and Computer Assisted Intervention, Academic Press, 2020, Pages 503-519, ISBN 9780128161760, https://doi.org/10.1016/B978-0-12-816176-0.00026-0.
• Z. Zhao, W. Chen, X. Wu, P.C.Y. Chen, J. Liu (2017), LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst., 11: 68-75. https://doi.org/10.1049/iet-its.2016.0208.
• I. Cinar, M. Koklu (2022). Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences. 10.15832/ankutbd.862482.
• Cevre, Sehircilik ve İklim Degisikligi Baskanligi - Ulusal Hava Kalitesi Izleme Agi, [Online]. Available: http://sim.csb.gov.tr/SERVICES/airquality. Accessed on: September 21, 2022.
• Y. Taspinar, I. Cinar, M. Koklu (2021). Prediction of Computer Type Using Benchmark Scores of Hardware Units. Selcuk University Journal of Engineering Science and Technology. 1. 11-17.