A Survey On Air Quality Prediction Using Traditional Statistics Method

Karthikeyani, S.; Rathi, Swaraj

doi:10.32628/cseit2063197

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…For shorter-term forecasting, ARIMA coupled with the Holt exponential smoothing model was utilized to forecast daily AQI values [22]. More recent work proposed a survey comparing classical statistical models for AQI forecasting and concluded that ARIMA models were superior in mapping trends and producing predictions with the lowest root mean square error (RMSE) compared to other statistical models [23]. Nevertheless, the majority of statistical-based models consider only previously recorded data to forecast the following ones without accounting for the effect of atmospheric variables and conditions.…”

Section: Related Workmentioning

confidence: 99%

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

et al. 2022

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Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution. This article proposes a comprehensive method to forecast AQIs. Initially, the work focused on predicting hourly ambient concentrations of PM2.5 and PM10 using artificial neural networks. Once the method was developed, the work was extended to the prediction of other criteria pollutants, i.e., O3, SO2, NO2, and CO, which fed into the process of estimating AQI. The prediction of the AQI not only requires the selection of a robust forecasting model, it also heavily relies on a sequence of pre-processing steps to select predictors and handle different issues in data, including gaps. The presented method dealt with this by imputing missing entries using missForest, a machine learning-based imputation technique which employed the random forest (RF) algorithm. Unlike the usual practice of using RF at the final forecasting stage, we utilized RF at the data pre-processing stage, i.e., missing data imputation and feature selection, and we obtained promising results. The effectiveness of this imputation method was examined against a linear imputation method for the six criteria pollutants and the AQI. The proposed approach was validated against ambient air quality observations for Al-Jahra, a major city in Kuwait. Results obtained showed that models trained using missForest-imputed data could generalize AQI forecasting and with a prediction accuracy of 92.41% when tested on new unseen data, which is better than earlier findings.

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Section: Related Workmentioning

confidence: 99%

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

et al. 2022

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Enhanced Jaya optimization for improving multilayer perceptron neural network in urban air quality prediction

Abu Doush,

Sultan,

Alsaber

et al. 2024

Journal of Intelligent Systems

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The multilayer perceptron (MLP) neural network is a widely adopted feedforward neural network (FNN) utilized for classification and prediction tasks. The effectiveness of MLP greatly hinges on the judicious selection of its weights and biases. Traditionally, gradient-based techniques have been employed to tune these parameters during the learning process. However, such methods are prone to slow convergence and getting trapped in local optima. Predicting urban air quality is of utmost importance to mitigate air pollution in cities and enhance the well-being of residents. The air quality index (AQI) serves as a quantitative tool for assessing the air quality. To address the issue of slow convergence and limited search space exploration, we incorporate an opposite-learning method into the Jaya optimization algorithm called EOL-Jaya-MLP. This innovation allows for more effective exploration of the search space. Our experimentation is conducted using a comprehensive 3-year dataset collected from five air quality monitoring stations. Furthermore, we introduce an external archive strategy, termed EOL-Archive-Jaya, which guides the evolution of the algorithm toward more promising search regions. This strategy saves the best solutions obtained during the optimization process for later use, enhancing the algorithm’s performance. To evaluate the efficacy of the proposed EOL-Jaya-MLP and EOL-Archive-Jaya, we compare them against the original Jaya algorithm and six other popular machine learning techniques. Impressively, the EOL-Jaya-MLP consistently outperforms all other methods in accurately predicting AQI levels. The MLP model’s adaptability to dynamic urban air quality patterns is achieved by selecting appropriate values for weights and biases. This leads to efficacy of our proposed approaches in achieving superior prediction accuracy, robustness, and adaptability to dynamic environmental conditions. In conclusion, our study shows the superiority of the EOL-Jaya-MLP over traditional methods and other machine learning techniques in predicting AQI levels, offering a robust solution for urban air quality prediction. The incorporation of the EOL-Archive-Jaya strategy further enhances the algorithm’s effectiveness, ensuring a more efficient exploration of the search space.

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A Survey On Air Quality Prediction Using Traditional Statistics Method

Cited by 2 publications

References 9 publications

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

Enhanced Jaya optimization for improving multilayer perceptron neural network in urban air quality prediction

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