Machine learning approach to predict and compare the air quality index in a confined environment

• Autorzy: Kumar Sampath Harish , Kanish Thorapadi Chandrasekaran

Identyfikatory

DOI

10.37190/epe240401

• Języki publikacji: EN

Abstrakty

EN

Indoor air pollution is very dangerous as people spend the majority time indoors. Cooking areas are found to be hazardous as there would be an emission of harmful pollutants. This is due to the continuous cooking process which affects people working there causing them various diseases, especially carbon monoxide poisoning. The purpose of this research is to evaluate several machine learning algorithms like support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), and decision tree (DT) for predicting the air quality index (AQI) of a Barbeque Nation Hotel kitchen’s confined interior environment. This investigation was done based on real-time data that was gathered by an indoor air quality monitoring system which was placed inside the kitchen for a few weeks under various cooking conditions. Results show that DT has the highest accuracy of 98.79% followed by KNN with an accuracy of 93.01%. SVM has an accuracy of 80.34%, and LR has a low accuracy of 80.20%. Therefore, DT which is a classification algorithm that comes under supervised machine learn-ing has predicted AQI accurately compared to others. Moreover, by segregating living from non-living particulate matter and nullifying them, airborne diseases like COVID-19 can be prevented in the future.

Słowa kluczowe

EN

air quality; disease; logistic regression; air quality indices; carbon monoxide poisoning; confined environment

PL

jakość powietrza; choroba; regresja logistyczna; indeks jakości powietrza; zatrucie tlenkiem węgla; środowisko zamknięte

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Environment Protection Engineering
• Rocznik: 2024
• Tom: Vol. 50, nr 4
• Strony: 5--27
• Opis fizyczny: Bibliogr. 45 poz., rys., tab.

Twórcy

autor

Kumar Sampath Harish

• School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore-632014, Tamil Nadu, India

• https://orcid.org/0009-0007-2124-0467

autor

Kanish Thorapadi Chandrasekaran

• School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore-632014, Tamil Nadu, India

• https://orcid.org/0000-0002-5381-4337

Bibliografia

• [1] ESPINOSA R., JIMÉNEZ F., PALMA J., Multi-objective evolutionary spatio-temporal forecasting of air pollution, Fut. Gen. Comp. Syst., 2022, 136, 15–33. DOI: 10.1016/j.future.2022.05.020.
• [2] MALTARE N.N., VAHORA S., Air quality index prediction using machine learning for Ahmeda-bad City, Dig. Chem. Eng., 2023, 7, 100093.
• [3] LEONG W.C., KELANI R.O., AHMAD Z., Prediction of air pollution index (API) using support vector machine (SVM), J. Environ. Chem. Eng., 2020, 8 (3), 103208. DOI: 10.1016/j.jece.2019.103208.
• [4] EJAZ A., MALIK A.O., LOPEZ-CANDALES A., Ambient air pollution and cardiovascular disease: learned from the COVID-19 pandemic, Postgrad. Med., 2021, 133 (6), 587, 588. DOI: 10.1080/00325481.2021.1921504.
• [5] LI Y., DU J., LIN S., HE H., JIA R., LIU W., Air pollution increased risk of reproductive system diseases: a 5-year outcome analysis of different pollutants in different seasons, ages, and genders, Environ. Sci. Poll. Res., 2022, 29 (5), 7312–7321. DOI: 10.1007/s11356-021-16238-7.
• [6] HE G., PAN Y., TANAKA T., The short-term impacts of COVID-19 lockdown on urban air pollution in China, Nat. Sust., 2020, 3 (12), 1005–1011. DOI: 10.1038/s41893-020-0581-y.
• [7] DONG D., XU B., SHEN N., HE Q., The adverse impact of air pollution on China’s economic growth, Sust., 2021, 13 (16), 9056. DOI: 10.3390/su13169056.
• [8] SHABAN K.B., KADRI A., REZK E., Urban air pollution monitoring system with forecasting models, IEEE Sens. J., 2016, 16 (8), 2598–2606. DOI: 10.1109/JSEN.2016.2514378.
• [9] WANG A., XU J., TU R., SALEH M., HATZOPOULOU M., Potential of machine learning for prediction of traffic-related air pollution, Trans. Res. D Trans. Environ., 2020, 88, 102599. DOI: 10.1016/j.trd.2020.102599.
• [10] CHEN L., MAO F., HONG J., ZANG L., CHEN J., ZHANG Y., GAN Y., GONG W., XU H., Improving PM2.5 predictions during COVID-19 lockdown by assimilating multi-source observations and adjusting emissions, Environ. Poll., 2022, 297, 118783. DOI: 10.1016/j.envpol.2021.118783.
• [11] GRELL G.A., PECKHAM S.E., SCHMITZ R., MCKEEN S.A., FROST G., SKAMAROCK W.C., EDER B., Fully coupled online chemistry within the WRF model, Atm. Environ., 2005, 39 (37), 6957–6975. DOI: 10.1016 /j.atmosenv.2005.04.027.
• [12] KE H., GONG S., HE J., ZHANG L., CUI B., WANG Y., MO J., ZHOU Y., ZHANG H., Development and application of an automated air quality forecasting system based on machine learning, Sci. Total Environ., 2022, 806, 151204. DOI: 10.1016/j.scitotenv.2021.151204.
• [13] WOOD D.A., Local integrated air quality predictions from meteorology (2015 to 2020) with machine and deep learning assisted by data mining, Sust. Anal. Model., 2022, 2, 100002. DOI: 10.1016 /j.samod.2021.100002.
• [14] MASMOUDI S., ELGHAZEL H., TAIEB D., YAZAR O., KALLEL A., A machine-learning framework for predicting multiple air pollutants’ concentrations via multi-target regression and feature selection, Sci. Total Environ., 2020, 715, 136991. DOI: 10.1016/j.scitotenv.2020.136991.
• [15] LI X., PENG L., YAO X., CUI S., HU Y., YOU C., CHI T., Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation, Environ. Poll., 2017, 231, 997–1004. DOI: 10.1016/j.envpol.2017.08.114.
• [16] ZHAO J., DENG F., CAI Y., CHEN J., Long short-term memory. Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction, Chemosphere, 2019, 220, 486–492. DOI: 10.1016/j.chemosphere. 2018.12.128.
• [17] LIU B., GUO X., LAI M., WANG Q., Air pollutant concentration forecasting using long short-term memory based on wavelet transform and information gain. A case study of Beijing, Comp. Int. Neurosci., 2020, 834699. DOI: 10.1155/2020/8834699.
• [18] NAVARES R., AZNARTE J.L., Predicting air quality with deep learning LSTM. Towards comprehensive models, Ecol. Inform., 2020, 55, 101019. DOI: 10.1016/j.ecoinf.2019.101019.
• [19] ATHIRA V., GEETHA P., VINAYAKUMAR R., SOMAN K.P., Deepairnet: Applying recurrent networks for air quality prediction, Procedia Comp. Sci., 2018, 132, 1394–1403. DOI: 10.1016/j.procs.2018.05.068.
• [20] CHANG Y.S., CHIAO H.T., ABIMANNAN S., HUANG Y.P., TSAI Y.T., LIN K.M., An LSTM-based aggre-gated model for air pollution forecasting, Atm. Poll. Res., 2020, 11 (8), 1451–1463. DOI: 10.1016 /j.apr.2020.05.015.
• [21] NATH P., SAHA P., MIDDYA A.I., ROY S., Long-term time-series pollution forecast using statistical and deep learning methods, Neural Comp. Appl., 2021, 1–20. DOI: 10.1007/s00521-021-05901-2.
• [22] BEKKAR A., HSSINA B., DOUZI S., DOUZI K., Air-pollution prediction in smart city, deep learning approach, J. Big Data., 2021, 8 (1), 1–21. DOI: 10.1186/s40537-021-00548-1.
• [23] GOPU P., PANDA R.R., NAGWANI N.K., Time series analysis using ARIMA model for air pollution pre-diction in Hyderabad city of India, Soft Comp. Sign. Proc.: Proc. 3rd ICSCSP 2020, Springer, Singa-pore 2021, 1, 47–56. DOI: 10.1007/978-981-33-6912-2_5.
• [24] MANI G., VOLETY R., A comparative analysis of LSTM and ARIMA for enhanced real-time air pollu-tant levels forecasting using sensor fusion with ground station data, Cogen. Eng., 2021, 8 (1), 1936886. DOI: 10.1080/23311916.2021.1936886.
• [25] LIU T., YOU S., Analysis and forecast of Beijing’s air quality index based on ARIMA model and neural network model, Atmosphere, 2022, 13 (4), 512. DOI: 10.3390/atmos13040512.
• [26] MALTARE N.N., VAHORA S., Air quality index prediction using machine learning for Ahmedabad city, Digit. Chem. Eng., 2023, 7, 100093. DOI: 10.1016/j.dche.2023.100093.
• [27] SHAH D.P., PATEL P., A comparison between national air quality index, India and composite air quality index for Ahmedabad, India, Environ. Chall., 2021, 5, 100356. DOI: 10.1016/j.envc.2021.100356.
• [28] CURTIS A.E., SMITH T.A., ZIGANSHIN B.A., ELEFTERIADES J.A., The mystery of the Z-score, Aorta, 2016, 4 (4), 124–130. DOI: 10.12945/j.aorta.2016.16.014.
• [29] EMMANUEL T., MAUPONG T., MPOELENG D., SEMONG T., MPHAGO B., TABONA O., A survey on missing data in machine learning, J. Big Data, 2021, 8 (1), 1–37. DOI: 10.1186/s40537-021-00516-9.
• [30] CHERVONENKIS A.Y., Early history of support vector machines, empirical inference. Festschrift in Honor of Vladimir N. Vapnik, 2013, 13–20. DOI: 10.1007/978-3-642-41136-6_3.
• [31] LEONG W.C., KELANI R.O., AHMAD Z., Prediction of air pollution index (API) using support vector machine (SVM), J. Environ. Chem. Eng., 2020, 8 (3), 103208. DOI: 10.1016/j.jece.2019.103208.
• [32] KE H., GONG S., HE J., ZHANG L., CUI B., WANG Y., MO J., ZHOU Y., ZHANG H., Development and appli-cation of an automated air quality forecasting system based on machine learning, Sci. Total Environ., 2022, 806, 151204. DOI: 10.1016/j.scitotenv.2021.151204.
• [33] ESPINOSA R., JIMÉNEZ F., PALMA J., Multi-objective evolutionary spatio-temporal forecasting of air pollution, Futur. Gen. Comp. Syst., 2022, 136, 15–33. DOI: 10.1016/j.future.2022.05.020.
• [34] BALOGUN A.L., TELLA A., Modeling and investigating the impacts of climatic variables on ozone concen-tration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression, Chemosphere, 2022, 299, 134250. DOI: 10.1016/j.chemosphere. 2022.134250.
• [35] SCHÖLKOPF B., SMOLA A.J., BACH F., Learning with kernels: support vector machines, regularization, optimization, and beyond, MIT Press, 2022.
• [36] ALTAY O., ULAS M., Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children, 6th International Symposium on Digital Foren-sic and Security (ISDFS), IEEE, 2018, 1–4. DOI: 10.1109/ISDFS.2018.8355354.
• [37] DAMANIK I.S., WINDARTO A.P., WANTO A., ANDANI S.R., SAPUTRAW., Decision tree optimization in C4.5 algorithms using genetic algorithm, J. Phys., Conf. Ser., IOP Publ., 2019, 1255 (1), 012012. DOI: 10.1088/1742-6596/1255/1/012012.
• [38] ROKACH M., ROKACH L., MAIMON O., Top-down induction of decision trees classifiers-a survey, IEEE Trans. Syst., Man, Cyber., Part C (Appl. Rev.), 2005, 35 (4), 476–487. DOI: 10.1109/TSMCC. 2004.843247.
• [39] SWAIN P.H., HAUSKA H., The decision tree classifier: Design and potential, IEEE Trans. Geosci. Electron., 1977, 15 (3), 142–147. DOI: 10.1109/TGE.1977.6498972.
• [40] ELSON J., TAILOR A., BANERJEE S., SALIM R., HILLABY K., JURKOVIC D., Expectant management of tubal ectopic pregnancy: prediction of successful outcome using decision tree analysis, Ultras. Obst. Gyn., 2004, 23 (6), 552–556. DOI: 10.1002/uog.1061.
• [41] CHIANG W.Y.K., ZHANG D., ZHOU L., Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression, Dec. Supp. Syst., 2006, 41 (2), 514–531. DOI: 10.1016/j.dss.2004.08.016.
• [42] NEVITT J., HANCOCK G.R., Improving the root mean square error of approximation for nonnormal conditions in structural equation modeling, J. Exp. Educ., 2000, 68 (3), 251–268. DOI: 10.1080 /00220970009600095.
• [43] CHICCO D., WARRENS M.J., JURMAN G., The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, Peer J. Comp. Sci., 2021, 7, 623. DOI: 10.7717/PEERJ-CS.623.
• [44] PRASANNAVENKATESAN T., JACOB I.J., RUBY A.U., VAMSIDHAR Y., Prediction of COVID-19 possibilities using KNN classification algorithm, Research Square, 2020. DOI: 10.21203/rs.3.rs-70985/v2.
• [45] WU H., YANG S., HUANG Z., HE J., WANG X., Type 2 diabetes mellitus prediction model based on data mining, Inf. Med. Unl., 2018, 10, 100–107. DOI: 10.1016/j.imu.2017.12.006