Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Abstract

Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. is article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concentrations based on atmospheric variables. In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks. Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model. Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model. In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.Hindawi

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

“…The behavior of air pollutants varies, as they are affected by various climatic and geographic variables [1,[7][8][9], in this paper we will limit ourselves to the study of particulate matter (PM), which is a classification of pollutants that have begun to generate alerts in growing and overpopulated cities because of their chaotic behavior, which is complicated to model [6,10]. Algorithms such as artificial neural networks (ANN) have demonstrated their versatility in modeling the chaotic behavior of several variables, allowing the prediction of future behaviors with great adaptability to changes in the analysis data [11][12][13][14][15][16][17][18].…”