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

Abstract

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.

Pm2.5 Time Series Imputation with Deep Learning and Interpolation

“…On the other hand, considering that related works used other datasets, and each dataset presents different characteristics, the comparison is carried out only for reference. According to Table 9, it can be seen that in terms of RMSE, the proposal is only below the work [20], which obtained an RMSE of 3.756 ug/m 3 ; in terms of R 2 , the proposal with R 2 of 0.6946 is below the work [22], which reported an R 2 of 0.895; and in terms of MAE, the proposal with MAE = 3.4944 ug/m 3 exceeded the work [21] with MAE = 8.31 ug/m 3 .…”

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

“…High data variance can cause a slower training process and lead to a drop into minimal local values. This results in unreliable and poor forecasting models 38 . Therefore, all parameters were normalized between 0 and 1 to obtain minimum RMSE using max‐min normalization (Equation ). where x i represents the actual data, x max for the maximum data, and x min for the minimum data.…”

“…The trained RF models' mean square error (MSE) is the basis for the stopping conditions. The imputation procedure stops when the MSE of iteration (i) exceeds the MSE of the previous iterations, that is, (i‐1), at which point the results are those obtained from the previous iteration 38,45,46 …”

“…This results in unreliable and poor forecasting models. 38 Therefore, all parameters were normalized between 0 and 1 to obtain minimum RMSE using max-min normalization (Equation 8).…”

Prediction of air quality index using auto machine learning tools over Andhra Pradesh state of India

“…To address this, missForest, a machine learning-based imputation strategy that used the random forest (RF) algorithm to impute missing information, was used. In addition, RF was used at the preprocessing stage, i.e., missing data imputation and feature selection, instead of employing RF at the nal forecasting stage, and yielded encouraging results (Alkabbani et al, 2022). Researching the in uencing variables of air quality is essential to control and avoid air pollution properly.…”