Accuracy improvement in air‐quality forecasting using regressor combination with missing data imputation

Abstract

This article proposes a hybrid model based on regressor combination to improve the accuracy of air-quality forecasting. The expectation-maximization algorithm was used to impute the missing values of the dataset. The optimal hyperparameter values for the regressors were found by the grid search approach, depending on the mean absolute error (MAE), in the training session. The regressors having the minimum MAE were then globally combined for prediction. The output of the regressor with the minimum absolute error between the actual and predicted values was chosen as the prediction result of the hybrid model. The performance of the proposed model was compared with that of sequential deep learning methods, namely long short-term memory and gated recurrent unit, in terms of MAE, mean relative error (MRE), and squared correlation coefficient (SCC) metrics. The imputed dataset was divided into training and testing subsets of different durations. According to the experimental results, our hybrid model performed better than the deep learning methods in terms of MAE, MRE, and SCC metrics, irrespective of the training data length. Furthermore, the Akaike's information criterion and the Bayesian information criterion values suggested that the 226

Machine learning algorithms to forecast air quality: a survey

“…The ARIMA-GCP model obtains the lowest error by far. Ozturk ( 2021 ) compares a hybrid model which combines RF, SVR and radial basis function regressor with an LSTM and a GRU to forecast CO and in a provided dataset. Results show that the hybrid model obtains the lowest error.…”

A Noise-Aware Multiple Imputation Algorithm for Missing Data

“…The other type of method is based on machine learning: the problem of missing data imputation has gradually attracted attention in machine learning and data mining. At present, the proposed methods include KNN [11][12][13], kernel [14,15], K-means [16,17], decision tree [18][19][20], regression [21], naive Bayes [22,23], Bayesian network [24], neural network [25][26][27][28], etc. In neural network imputation, the attributes are imputed variables, and related attributes are input vectors.…”