An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach

Samal, K. Krishna Rani; Panda, Ankit Kumar; Babu, Korra Sathya; Das, Santos Kumar

doi:10.1016/j.scs.2021.102923

Cited by 39 publications

(16 citation statements)

References 55 publications

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“…The SVM with the 2nd order polynomial-imputation technique, reported by [27], had an RMSE value of 184 µg/m 3 . Another multivariate imputation technique using the k-nearest neighbors (K-NN) algorithm was used in the comparative study in [45] before forecasting the PM 2.5 levels using different effective and robust deep learning (DL) methodologies. In that study, different missing rates were tested to confirm the reliability of K-NN imputation for further validation.…”

Section: Particulate Matter-25 (Pm25)mentioning

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: Particulate Matter-25 (Pm25)mentioning

confidence: 99%

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

et al. 2022

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“…It occurs most frequently in air pollutant research studies because the data are measured by air quality monitoring stations at regular time intervals and there may be reading or recording failures. These failures may be due to maintenance shutdowns, filter clogging, periodic calibrations, power failures, etc., resulting in the absence of measurements at certain time intervals [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

et al. 2022

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The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.

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“…In addition, air pollution has been the main cause of death in the United States for nearly 25 years [5]. An accurate, effective and stable air quality index (AQI) prediction model is necessary, to promote urban public health and the sustainable development of society [6]. As the basis of AQI estimation, individual air quality index (IAQI) provides technical regulation from a single pollutant concentration (PM 2.5 , PM 10 , NO 2 , etc.)…”

Section: Introductionmentioning

confidence: 99%

“…perspective. The modeling of fine-grained IAQI forecasting can be regarded as a traditional time series prediction problem, and attracts massive attention from researchers and policymakers [6,7].…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

et al. 2022

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Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.

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An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach

Cited by 39 publications

References 55 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

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

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