Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model

Concentration Separation Prediction Model to Enhance Prediction Accuracy of Particulate Matter

Demand for more accurate particulate matter forecasts is accumulating owing to the increased interest and issues regarding particulate matter. Incredibly low concentration particulate matter, which accounts for most of the overall particulate matter, is often underestimated when a particulate matter prediction model based on machine learning is used. This study proposed a concentration-specific separation prediction model to overcome this shortcoming. Three prediction models based on Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), commonly used for performance evaluation of the proposed prediction model, were used as comparative models. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and accuracy were utilized for performance evaluation. The results showed that the prediction accuracy for all Air Quality Index (AQI) segments was more than 80 percent in the entire concentration spectrum. In addition, the study confirmed that the over-prediction phenomenon of single neural network models concentrated in the ‘normal’ AQI region was alleviated