Daily PM10 Prediction of Thiruvananthapuram City and Interpretability Analysis of Influencing factors

Abbas, R., Shehata, N., Mohamed, E. A., Salah, H., & Abdelzaher, M. (2021). Environmental safe disposal of cement kiln dust for the production of ‎geopolymers. Egyptian Journal of Chemistry. https://doi.org/10.21608/ejchem.2021.89060.4276
Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, 135, 110114. https://doi.org/10.1016/j.rser.2020.110114
Aiswarya, R., Resmi, A. R., Rahsha, C. T., Dharman, S., Adarsh, S., & Mamatha, M. (2023). Analyzing the Effect of Air Pollutants on Particulate Matter Concentrations of the Tropical coastal city of Thiruvananthapuram, India by Wavelet Coherence. IOP Conference Series: Earth and Environmental Science, 1237(1), 012017. https://doi.org/10.1088/1755-1315/1237/1/012017
Alsaqr, A. M. (2021). Remarks on the use of Pearson’s and Spearman’s correlation coefficients in assessing relationships in ophthalmic data. African Vision and Eye Health, 80(1), Article 1. https://doi.org/10.4102/aveh.v80i1.612
Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360–3379. https://doi.org/10.1080/10494820.2021.1928235
Bellinger, C., Mohomed Jabbar, M. S., Zaïane, O., & Osornio-Vargas, A. (2017). A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17(1), 907. https://doi.org/10.1186/s12889-017-4914-3
Bharat, A., Pooja, N., & Reddy, R. A. (2018). Using Machine Learning algorithms for breast cancer risk prediction and diagnosis. 2018 3rd International Conference on Circuits, Control, Communication and Computing (I4C), 1–4. https://doi.org/10.1109/CIMCA.2018.8739696
Bhatt, C. M., Patel, P., Ghetia, T., & Mazzeo, P. L. (2023). Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms, 16(2), Article 2. https://doi.org/10.3390/a16020088
Blenkinsop, S., Chan, S. C., Kendon, E. J., Roberts, N. M., & Fowler, H. J. (2015). Temperature influences on intense UK hourly precipitation and dependency on large-scale circulation. Environmental Research Letters, 10(5), 054021. https://doi.org/10.1088/1748-9326/10/5/054021
Brokamp, C., Jandarov, R., Hossain, M., & Ryan, P. (2018). Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model. Environmental Science & Technology, 52(7), 4173–4179. https://doi.org/10.1021/acs.est.7b05381
Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242. https://doi.org/10.1016/S0140-6736(02)11274-8
Chaibi, M., Benghoulam, E. M., Tarik, L., Berrada, M., & Hmaidi, A. E. (2021). An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction. Energies, 14(21), Article 21. https://doi.org/10.3390/en14217367
Dongarrà, G., Manno, E., Varrica, D., Lombardo, M., & Vultaggio, M. (2010). Study on ambient concentrations of PM10, PM10–2.5, PM2.5 and gaseous pollutants. Trace elements and chemical speciation of atmospheric particulates. Atmospheric Environment, 44(39), 5244–5257. https://doi.org/10.1016/j.atmosenv.2010.08.041
Espinheira, P. L., Silva, L. C. M., & Cribari-Neto, F. (2021). Bias and variance residuals for machine learning nonlinear simplex regressions. Expert Systems with Applications, 185, 115656. https://doi.org/10.1016/j.eswa.2021.115656
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1
Huang, X., Zhang, J., Zhang, W., Tang, G., & Wang, Y. (2021). Atmospheric ammonia and its effect on PM2.5 pollution in urban Chengdu, Sichuan Basin, China. Environmental Pollution, 291, 118195. https://doi.org/10.1016/j.envpol.2021.118195
Huffman, M. D., Prabhakaran, D., Osmond, C., Fall, C. H. D., Tandon, N., Lakshmy, R., Ramji, S., Khalil, A., Gera, T., Prabhakaran, P., Biswas, S. K. D., Reddy, K. S., Bhargava, S. K., & Sachdev, H. S. (2011). Incidence of Cardiovascular Risk Factors in an Indian Urban Cohort. Journal of the American College of Cardiology, 57(17), 1765–1774. https://doi.org/10.1016/j.jacc.2010.09.083
Kabiraj, S., Raihan, M., Alvi, N., Afrin, M., Akter, L., Sohagi, S. A., & Podder, E. (2020). Breast Cancer Risk Prediction using XGBoost and Random Forest Algorithm. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–4. https://doi.org/10.1109/ICCCNT49239.2020.9225451
Kim, B.-Y., Lim, Y.-K., & Cha, J. W. (2022). Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms. Atmospheric Pollution Research, 13(10), 101547. https://doi.org/10.1016/j.apr.2022.101547
Kujawska, J., Kulisz, M., Oleszczuk, P., & Cel, W. (2022). Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland. Energies, 15(17), Article 17. https://doi.org/10.3390/en15176428
Kumar, R. S., & Swarnalatha, K. (2019). Prediction of vehicular exhaust emission for Thiruvananthapuram city. In Recent Advances in Materials, Mechanics and Management. CRC Press.
Lavanyaa, V. P., Harshitha, K. M., Beig, G., & Srikanth, R. (2023). Background and baseline levels of PM2.5 and PM10 pollution in major cities of peninsular India. Urban Climate, 48, 101407. https://doi.org/10.1016/j.uclim.2023.101407
Li, X., Ma, Y., Wang, Y., Liu, N., & Hong, Y. (2017). Temporal and spatial analyses of particulate matter (PM10 and PM2.5) and its relationship with meteorological parameters over an urban city in northeast China. Atmospheric Research, 198, 185–193. https://doi.org/10.1016/j.atmosres.2017.08.023
Li, X., Wu, C., Meadows, M. E., Zhang, Z., Lin, X., Zhang, Z., Chi, Y., Feng, M., Li, E., & Hu, Y. (2021). Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China. Remote Sensing, 13(15), Article 15. https://doi.org/10.3390/rs13153011
Lin, L., Liang, Y., Liu, L., Zhang, Y., Xie, D., Yin, F., & Ashraf, T. (2022). Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China. Remote Sensing, 14(20), Article 20. https://doi.org/10.3390/rs14205239
Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
Murray, C. J., & Lopez, A. D. (1997). Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. The Lancet, 349(9064), 1498–1504. https://doi.org/10.1016/S0140-6736(96)07492-2
Nishanth, T., Praseed, K. M., Rathnakaran, K., Satheesh Kumar, M. K., Ravi Krishna, R., & Valsaraj, K. T. (2012). Atmospheric pollution in a semi-urban, coastal region in India following festival seasons. Atmospheric Environment, 47, 295–306. https://doi.org/10.1016/j.atmosenv.2011.10.062
Nistane, V., & Harsha, S. (2018). Performance evaluation of bearing degradation based on stationary wavelet decomposition and extra trees regression. World Journal of Engineering, 15(5), 646–658. https://doi.org/10.1108/WJE-12-2017-0403
Plocoste, T., & Laventure, S. (2023). Forecasting PM10 Concentrations in the Caribbean Area Using Machine Learning Models. Atmosphere, 14(1), Article 1. https://doi.org/10.3390/atmos14010134
Pope III, C. A. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. JAMA, 287(9), 1132. https://doi.org/10.1001/jama.287.9.1132
Prasad, N. R., Patel, N. R., & Danodia, A. (2021). Crop yield prediction in cotton for regional level using random forest approach. Spatial Information Research, 29(2), 195–206. https://doi.org/10.1007/s41324-020-00346-6
Puri, N., Prasad, H. D., & Jain, A. (2018). Prediction of Geotechnical Parameters Using Machine Learning Techniques. Procedia Computer Science, 125, 509–517. https://doi.org/10.1016/j.procs.2017.12.066
Rajput, S., Kapdi, R. A., Raval, M. S., & Roy, M. (2023). Interpretable machine learning model to predict survival days of malignant brain tumor patients. Machine Learning: Science and Technology, 4(2), 025025. https://doi.org/10.1088/2632-2153/acd5a9
Riches, N. O., Gouripeddi, R., Payan-Medina, A., & Facelli, J. C. (2022). K-means cluster analysis of cooperative effects of CO, NO2, O3, PM2.5, PM10, and SO2 on incidence of type 2 diabetes mellitus in the US. Environmental Research, 212, 113259. https://doi.org/10.1016/j.envres.2022.113259
Seyyedattar, M., Ghiasi, M. M., Zendehboudi, S., & Butt, S. (2020). Determination of bubble point pressure and oil formation volume factor: Extra trees compared with LSSVM-CSA hybrid and ANFIS models. Fuel, 269, 116834. https://doi.org/10.1016/j.fuel.2019.116834
Sohrab, S., Csikós, N., & Szilassi, P. (2024). Effect of geographical parameters on PM10 pollution in European landscapes: A machine learning algorithm-based analysis. Environmental Sciences Europe, 36(1), 152. https://doi.org/10.1186/s12302-024-00972-z
Stojić, A. (2021). Meteorological Factors Governing Particulate Matter Distribution in an Urban Environment. Sinteza 2021 - International Scientific Conference on Information Technology and Data Related Research, 89–93. https://doi.org/10.15308/Sinteza-2021-89-93
Suleiman, A., Tight, M. R., & Quinn, A. D. (2019). Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2.5). Atmospheric Pollution Research, 10(1), 134–144. https://doi.org/10.1016/j.apr.2018.07.001
Sumesh, R. K., Rajeevan, K., Resmi, E. A., & Unnikrishnan, C. K. (2017). Particulate Matter Concentrations in the Southern Tip of India: Temporal Variation, Meteorological Influences, and Source Identification. Earth Systems and Environment, 1(2), 13. https://doi.org/10.1007/s41748-017-0015-9
Surendran, S., Mohan, A., Valamparampil, M. J., Nair, S., Balakrishnan, S. K., Laila, A. A., Reghunath, R., Jose, C., Rajeevan, A., Vasudevakaimal, P., Surendrannair, A. T., Nujum, Z. T., Varghese, S., & Mohan, A. (2022). Spatial analysis of chronic obstructive pulmonary disease and its risk factors in an urban area of Trivandrum, Kerala, India. Lung India, 39(2), 110. https://doi.org/10.4103/lungindia.lungindia_454_21
Tong, X., Ho, J. M. W., Li, Z., Lui, K.-H., Kwok, T. C., Tsoi, K. K., & Ho, K. F. (2020). Prediction model for air particulate matter levels in the households of elderly individuals in Hong Kong. Science of The Total Environment, 717, 135323.
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2023). Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction. Travel Behaviour and Society, 31, 78–92. https://doi.org/10.1016/j.tbs.2022.11.006
Verma, P., Verma, R., Mallet, M., Sisodiya, S., Zare, A., Dwivedi, G., & Ristovski, Z. (2024). Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms. Atmospheric Pollution Research, 15(6), 102123. https://doi.org/10.1016/j.apr.2024.102123
Wang, P., Liu, Y., Qin, Z., & Zhang, G. (2015). A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Science of The Total Environment, 505, 1202–1212. https://doi.org/10.1016/j.scitotenv.2014.10.078
Wang, S., Ren, Y., & Xia, B. (2023). PM2.5 and O3 concentration estimation based on interpretable machine learning. Atmospheric Pollution Research, 14(9), 101866. https://doi.org/10.1016/j.apr.2023.101866
Wehenkel, L., Ernst, D., & Geurts, P. (2006). Ensembles of extremely randomized trees and some generic applications. https://orbi.uliege.be/handle/2268/13447
WHO Regional Office for Europe. (2013). Review of evidence on health aspects of air pollution – REVIHAAP Project: Technical Report. WHO Regional Office for Europe. http://www.ncbi.nlm.nih.gov/books/NBK361805/
Wu, X., Wang, Y., He, S., & Wu, Z. (2020). PM 2.5/PM 10 ratio prediction based on a long short-term memory neural network in Wuhan, China. Geoscientific Model Development, 13(3), 1499–1511.
Wu, Y., Lin, S., Shi, K., Ye, Z., & Fang, Y. (2022). Seasonal prediction of daily PM2.5 concentrations with interpretable machine learning: A case study of Beijing, China. Environmental Science and Pollution Research, 29(30), 45821–45836. https://doi.org/10.1007/s11356-022-18913-9
Xi, X., Wei, Z., Xiaoguang, R., Yijie, W., Xinxin, B., Wenjun, Y., & Jin, D. (2015). A comprehensive evaluation of air pollution prediction improvement by a machine learning method. 2015 IEEE International Conference on Service Operations And Logistics, And Informatics (SOLI), 176–181. https://doi.org/10.1109/SOLI.2015.7367615
Yarveicy, H., & Ghiasi, M. M. (2017). Modeling of gas hydrate phase equilibria: Extremely randomized trees and LSSVM approaches. Journal of Molecular Liquids, 243, 533–541. https://doi.org/10.1016/j.molliq.2017.08.053
Zhang, L., Ji, Y., Liu, T., & Li, J. (2020). PM2.5 Prediction Based on XGBoost. 2020 7th International Conference on Information Science and Control Engineering (ICISCE), 1011–1014. https://doi.org/10.1109/ICISCE50968.2020.00207
Zhang, Y., Sun, Q., Liu, J., & Petrosian, O. (2024). Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors. Sustainability, 16(1), Article 1. https://doi.org/10.3390/su16010019
Zheng, G., Zhang, Y., Yue, X., & Li, K. (2023). Interpretable prediction of thermal sensation for elderly people based on data sampling, machine learning and SHapley Additive exPlanations (SHAP). Building and Environment, 242, 110602. https://doi.org/10.1016/j.buildenv.2023.110602
Zhou, J., Asteris, P. G., Armaghani, D. J., & Pham, B. T. (2020). Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dynamics and Earthquake Engineering, 139, 106390. https://doi.org/10.1016/j.soildyn.2020.106390
Zhou, Y., Yue, Y., Bai, Y., & Zhang, L. (2020). Effects of Rainfall on PM2.5 and PM10 in the Middle Reaches of the Yangtze River. Advances in Meteorology, 2020(1), 2398146. https://doi.org/10.1155/2020/2398146