Deep Learning Based Air Quality Prediction: A Case Study for London

Yıl 2022, Cilt: 11 Sayı: 4, 126 - 134, 28.12.2022

Anıl Utku Ümit Can

https://doi.org/10.46810/tdfd.1201415

Cited By: 1

Öz

Although states take various measures to prevent air pollution, air pollutants continue to exist as an important problem in the world. One air pollutant that seriously affects human health is called PM2.5 (particles smaller than 2.5 micrometers in diameter). These particles pose a serious threat to human health. For example, it can penetrate deep into the lung, irritate and erode the alveolar wall and consequently impair lung function. From this, the event PM2.5 prediction is very important. In this study, PM2.5 prediction was made using 12 models, namely, Decision Tree (DT), Extra Tree (ET), k-Nearest Neighbourhood (k-NN), Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.

Anahtar Kelimeler

Deep learning, London, PM_2.5 prediction, Machine learning, Air quality prediction

Kaynakça

• [1] Xing YF, Xu YH, Shi MH, The impact of PM2. 5 on the human respiratory system. J. Thorac. Dis. 2016;8(1), E69. https://doi.org/ 10.3978/j.issn.2072-1439.2016.01.19Lian YX.
• [2] Hayes RB, Lim C, Zhang Y, Cromar K, Shao Y, Reynolds HR, et al. PM2. 5 air pollution and cause-specific cardiovascular disease mortality. Int. J. Epidemiol. 2020;49(1), 25-35.
• [3] He K, Yang F, Ma Y, Zhang Q, Yao X, Chan CK, et al. The characteristics of PM2. 5 in Beijing, China. Atmos. Environ. 2001; 35(29), 4959-4970. https://doi.org/10.1016/S1352-2310(01)00301-6
• [4] Ma J, Yu Z, Qu Y, Xu J, Cao Y. Application of the XGBoost machine learning method in PM2. 5 prediction: A case study of Shanghai. Aerosol Air Qual. Res. 2020; 20(1), 128-138. https://doi.org/10.4209/aaqr.2019.08.0408
• [5] Masood A, Ahmad K. A model for particulate matter (PM2. 5) prediction for Delhi based on machine learning approaches. Procedia Comput. Sci. 2020; 167, 2101-2110. https://doi.org/10.1016/j.procs.2020.03.258
• [6] Danesh Yazdi M, Kuang Z, Dimakopoulou K, Barratt B, Suel E, Amini H, et al. Predicting fine particulate matter (PM2. 5) in the greater London area: an ensemble approach using machine learning methods. Remote Sens. 2020; 12(6), 914. https://doi.org/10.3390/rs12060914
• [7] Feng L, Yang T, Wang Z. Performance evaluation of photographic measurement in the machine-learning prediction of ground PM2. 5 concentrations. Atmos. Environ. 2021;262, 118623. https://doi.org/10.1016/j.atmosenv.2021.118623
• [8] Lv L, Wei P, Li J, Hu J. Application of machine learning algorithms to improve numerical simulation prediction of PM2. 5 and chemical components. Atmos. Pollut. Res. 2021; 12(11), 101211. https://doi.org/10.1016/j.apr.2021.101211
• [9] Enebish T, Chau K, Jadamba B, Franklin M. Predicting ambient PM2. 5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches. J. Exposure Sci. Environ. Epidemiol. 2021; 31(4), 699-708. https://doi.org/10.1038/s41370-020-0257-8
• [10] Karimian H, Li Q, Wu C, Qi Y, Mo Y, Chen G, et al. Evaluation of different machine learning approaches to forecasting PM2. 5 mass concentrations. Aerosol Air Qual. Res. 2019; 19(6), 1400-1410. https://doi.org/10.4209/aaqr.2018.12.0450
• [11] Pak U, Ma J, Ryu U, Ryom K, Juhyok U, Pak K, et al. Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China. Sci. Total Environ. 2020;699, 133561. https://doi.org/10.1016/j.scitotenv.2019.07.367
• [12] Xiao Q, Chang HH, Geng G, Liu Y. An ensemble machine-learning model to predict historical PM2. 5 concentrations in China from satellite data. Environ. Sci. Technol. 2018;52(22), 13260-13269. https://doi.org/10.1021/acs.est.8b0291
• [13] Kleine Deters J, Zalakeviciute R, Gonzalez M, Rybarczyk Y. Modeling PM2. 5 urban pollution using machine learning and selected meteorological parameters. J. Electr. Comput. Eng. 2017: 5106045. https://doi.org/10.1155/2017/5106045
• [14] Pollution PM2.5 data London 2019 Jan to Apr. Access time: 10 September 2022. https://www.kaggle.com/siddharthnobell/pollution-pm25-data-london-2019-jan-to-apr
• [15] Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. J. Appl. Sci. Technol. Trends. 2021; 2(01), 20-28. https://doi.org/10.38094/jastt20165
• [16] Brijain M, Patel R, Kushik MR, Rana K. A survey on decision tree algorithm for classification. Int. J. Eng. Dev. Res. 2014;2(1).
• [17] Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach. Learn. 2006;63(1), 3-42. https://doi.org/10.1007/s10994-006-6226-1
• [18] Sharaff A, Gupta H. Extra-tree classifier with metaheuristics approach for email classification. In Advances in computer communication and computational sciences. 2019. https://doi.org/189-197. 10.1007/978-981-13-6861-5_17
• [19] Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory, 1967;13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
• [20] Ali N, Neagu D, Trundle P. Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets. SN Appl. Sci. 2019; 1(12), 1-15. https://doi.org/10.1007/s42452-019-1356-9
• [21] Ertuğrul ÖF, Tağluk ME. A novel version of k nearest neighbor: Dependent nearest neighbor. Appl. Soft Comput, 2017;55,480-490. https://doi.org/10.1016/j.asoc.2017.02.020
• [22] Vapnik VN. Statistical learning theory. Wiley;1998.
• [23] Su X, Yan X, Tsai CL. Linear regression. Wiley Interdiscip. Rev. Comput Stat. 2012;4(3), 275-294. https://doi.org/10.1002/wics.1198
• [24] Ho TK. Random decision forests. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, Montreal, Canada, 1995. pp. 278–282.
• [25] Biau G, Scornet E. A random forest guided tour. Test. 2016;25(2), 197-227. https://doi.org/10.1007/s11749-016-0481-7
• [26] Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V. Support vector regression machines. Adv. Neural Inf. Process. Syst. 1997; 9, 155-161.
• [27] Pisner DA, Schnyer DM. Support vector machine. Mach. Learn. 2020. https://doi.org/10.1016/b978-0-12-815739-8.00006-7
• [28] Suthaharan S. Support vector machine. Machine learning models and algorithms for big data classification, Springer, Boston, MA, 2016. pp. 207-235.
• [29] Chen T, Guestrin C. XGBoost. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016. pp. 785–794.
• [30] Osman AIA, Ahmed AN, Chow MF, Huang YF, El-Shafie A. Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Eng. J. 2021; 12(2), 1545-1556. https://doi.org/10.1016/j.asej.2020.11.011
• [31] Sagi O, Rokach L. Approximating XGBoost with an interpretable decision tree. Inf. Sci. 2021;572, 522-542. https://doi.org/10.1016/j.ins.2021.05.055
• [32] Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clin. eHealth. 2021; 4, 1-11. https://doi.org/10.1016/j.ceh.2020.11.002
• [33] Abirami S, Chitra P. Energy-efficient edge based real-time healthcare support system. In Advances in computers. Elsevier; 2020, Vol. 117, No. 1, pp. 339-368. https://doi.org/10.1016/bs.adcom.2019.09.007
• [34] Fukushima K. Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Netw. 1988; 1, 119–130.
• [35] Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data. 2021;8(1), 1-74. https://doi.org/10.1186/s40537-021-00444-8
• [36] Botalb A, Moinuddin M, Al-Saggaf UM, Ali SS. Contrasting convolutional neural network (CNN) with multi-layer perceptron (MLP) for big data analysis. In 2018 International conference on intelligent and advanced system (ICIAS), Kuala Lumpur, Malaysia: IEEE; 2018. pp. 1-5. https://doi.org/10.1109/ICIAS.2018.8540626
• [37] Sutskever I, Martens J, Hinton GE. Generating text with recurrent neural networks. In ICML. 2011.
• [38] Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31(7), 1235-1270. https://doi.org/10.1162/neco_a_01199
• [39] Yang S, Yu X, Zhou Y. Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example. In 2020 International workshop on electronic communication and artificial intelligence (IWECAI). Shanghai, China: IEEE; 2020. pp. 98-101. https://doi.org/ 10.3978/10.1109/IWECAI50956.2020.00027
• [40] Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, et al. A state-of-the-art survey on deep learning theory and architectures. Electron. 2019;8(3), 292. https://doi.org/10.3390/electronics8030292
• [41] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
• [42] Smagulova K, James AP. A survey on LSTM memristive neural network architectures and applications. Eur. Phys. J. Spec. Top. 2019;228(10), 2313-2324.