PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Hu, Y., Scavia, D., & Kerkez, B. (2018). Are all data useful? Inferring causality to predict flows across sewer and drainage systems using directed information and boosted regression trees. Water Research, 145, 697-706.

• Lu, J., Zhang, Y., Chen, M., Wang, L., Zhao, S., Pu, X., & Chen, X. (2021). Estimation of monthly 1 km resolution PM2.5 concentrations using a random forest model over “2 + 26” cities, China. Urban Climate, 35, Article 100734. https://doi.org/10.1016/j.uclim.2020.100734

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• Sekar, C., Gurjar, B. R., Ojha, C. S. P., & Goyal, M. K. (2016). Potential assessment of neural network and decision tree algorithms for forecasting ambient PM2.5 and CO concentrations: Case study. Journal of Hazardous, Toxic, and Radioactive Waste, 20(4), 1-9. https://doi.org/10.1061/(asce)hz.2153-5515.0000276

• Shaadan, N., Rusdi, M. S., Azmi, N. N. S. N. M., Talib, S. F., & Azmi, W. A. W. (2019). Time series model for Carbon Monoxide (CO) at several industrial sites in Peninsular Malaysia. Malaysian Journal of Computing (MJoC), 4(1), 246-260.

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• Shams, S. R., Jahani, A., Moeinaddini, M., & Khorasani, N. (2020). Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression. Modeling Earth Systems and Environment, 6(3), 1467-1475. https://doi.org/10.1007/s40808-020-00762-5

• Shaziayani, W. N., Ul-Saufie, A. Z., Ahmat, H., & Al-Jumeily, D. (2021). Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration. Air Quality, Atmosphere and Health, 14, 1647-1663. https://doi.org/10.1007/s11869-021-01045-3

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• Watson, G. L., Telesca, D., Reid, C. E., Pfister, G. G., & Jerrett, M. (2019). Machine learning models accurately predict ozone exposure during wildfire events. Environmental Pollution, 254, Article 112792. https://doi.org/10.1016/j.envpol.2019.06.088