Deep Learning Models for Air Pollution Forecasting in Seoul South Korea

Kiftiyani, Usfita; Nazhifah, Sri Azizah

doi:10.1109/icsecs52883.2021.00106

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Sethi et al [12] proposed a method to predict the concentration of PM2. Enebish et al, [8] Kiftiyani & Nazhifah [21] Masood & Ahmad [9] Usmani [10] Bozdag [11] Sethi et al [12] Sharma et al [22] Juarez & Petersen [23] Sharma et al [31] Castelli et al [32] Asgari et al [33] Chen et al [34] Gocheva-Ilieva et al [35] Lee et al [13] Doresawamy et al [14] Ma et al [15] Bhalgat et al [24] Shen et al [25] Ma et al [40] Bouzoukis et al [36] Bouzoukis et al [36] Masmoudi et al [37] Khan et al [26] Kanjo [27] Lepperod [29] Peng et al [38] Zhang et al [17] Rubal et al [28] Liu et al [39] Fu et al [ LR gave promising results, and after that, CBL was applied to selected features in which RF improved the veracity in predicting PM2.5…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution

Ashraf¹,

Akram²,

Usman³

2022

Research Journal Of Computer Science And Information Technology

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The modern and motorized way of life has cultured air pollution. Air pollution has become the biggest rival of robust living. This situation is becoming more lethal in developing countries and so in Pakistan. Hence, this inquiry was carried out to propose an architecture design that could make real-time prediction of air pollution with another purpose of scanning the frequently adopted algorithm in past investigations. In addition, it was also intended to narrate the toxic effects of air pollution on human health. So, this research was carried out on a large dataset of Seoul as an adequate dataset of Pakistan was not attainable. The dataset consisted of three years (2017-2019) including 647,512 instances and 11 attributes. The four distinctive algorithms termed Random Forest, Linear Regression, Decision Tree and XGBoosting were employed. It was inferred that XGB is more promising and feasible in predicting concentration level of NO2, O3, SO2, PM10, PM2.5 and CO with the lowest RMSE and MAE values of 0.0111, 0.0262, 0.0168, 49.64, 41.68 and 0.1856 and 0.0067, 0.0096, 0.0017, 12.28, 7.63 and 0.0982 respectively. Furthermore, it was found out as well that the Random Forest was preferred mostly in the previous studies related to air pollution prophecy while many probes supported that air pollution is very detrimental to human health especially long-lasting exposure causes lung cancer, respiratory and cardiovascular diseases.

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution

Ashraf¹,

Akram²,

Usman³

2022

Research Journal Of Computer Science And Information Technology

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Machine learning algorithms to forecast air quality: a survey

2023

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Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

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