<abstract xmlns="http://www.w3.org/1999/xhtml">

Particulate Matter 2.5 (PM2.5) is a major contributor to air pollution and its exposure has substantial health consequences. As a result, precise prediction of PM2.5 concentrations is required in order to establish emission reduction strategies for air quality management. The article presents an Artificial Neural Network (ANN) model to forecast PM2.5 levels in a particular region. The model uses data such as air temperature, carbon monoxide, nitric oxide, nitrogen dioxide, ozone, suspended particles, rainfall, relative humidity, sulfur dioxide, wind direction and wind speed to predict PM2.5 concentrations in the air accurately. The model’s efficacy is evaluated using statistical measures such as the Coefficient of Determination, the Root Mean Squared Error and the Mean Absolute Error. The study results indicate that the ANN model outperforms more traditional statistical models, with R2 values of 0.987, which is higher than the values achieved by the Linear Regression and Decision Tree Regressor models, which are 0.88 and 0.89 respectively. The study’s findings have significant implications for public health and environmental policy, as they can provide more accurate and rapid statistics on air quality. The ability to forecast PM2.5 concentrations can help policymakers and health professionals take proactive measures to mitigate the impact of air pollution on public health.
</abstract>

Particulate Matter 2.5 (PM2.5) is a major contributor to air pollution and its exposure has substantial health consequences. As a result, precise prediction of PM2.5 concentrations is required in order to establish emission reduction strategies for air quality management. The article presents an Artificial Neural Network (ANN) model to forecast PM2.5 levels in a particular region. The model uses data such as air temperature, carbon monoxide, nitric oxide, nitrogen dioxide, ozone, suspended particles, rainfall, relative humidity, sulfur dioxide, wind direction and wind speed to predict PM2.5 concentrations in the air accurately. The model’s efficacy is evaluated using statistical measures such as the Coefficient of Determination, the Root Mean Squared Error and the Mean Absolute Error. The study results indicate that the ANN model outperforms more traditional statistical models, with R2 values of 0.987, which is higher than the values achieved by the Linear Regression and Decision Tree Regressor models, which are 0.88 and 0.89 respectively. The study’s findings have significant implications for public health and environmental policy, as they can provide more accurate and rapid statistics on air quality. The ability to forecast PM2.5 concentrations can help policymakers and health professionals take proactive measures to mitigate the impact of air pollution on public health.

Automated neural network forecast of PM2.5 concentration

Vijayanagara Sri Krishnadevaraya University

Department of Studies in Computer Science

International Journal of Mathematics and Computer in Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

Automated neural network forecast of PM concentration

{"article-title":{"sub":2.5,"content":["Automated neural network forecast of PM","concentration"]}}

Particulate Matter 2.5 (PM2.5) is a major contributor to air pollution and its exposure has substantial health consequences. As a result, precise prediction of...

Automated neural network forecast of PM_2.5 concentration

Pubblicato online: 09 giu 2023

Pagine: 67 - 78

Ricevuto: 23 apr 2023

Accettato: 18 mag 2023

DOI: https://doi.org/10.2478/ijmce-2023-0005

Parole chiave
Artificial neural network, particulate matter 2.5, root mean square error, mean absolute error, coefficient of determination

© 2023 Kerehalli Vinayaka Prasad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Automated neural network forecast of PM2.5 concentration

Pubblicato online: 09 giu 2023

Pagine: 67 - 78

Ricevuto: 23 apr 2023

Accettato: 18 mag 2023

DOI: https://doi.org/10.2478/ijmce-2023-0005

Parole chiaveArtificial neural network, particulate matter 2.5, root mean square error, mean absolute error, coefficient of determination

© 2023 Kerehalli Vinayaka Prasad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Automated neural network forecast of PM_2.5 concentration

Parole chiave
Artificial neural network, particulate matter 2.5, root mean square error, mean absolute error, coefficient of determination