Journal of Natural Environment

Comparison of the accuracy of the support vector regression model with two common methods of artificial neural network and adaptive neuro-fuzzy inference system in predicting the pollutant concentration of the PM10

Document Type : Research Paper

Authors

1 Department of Agriculture, College of Engineering, Yazd Branch, Yazd Islamic Azad University, Yazd, Iran

2 Associate Professor of Environmental Science, School of Natural Resources and Desert Studies, Yazd University.

3 Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran

Abstract

Abstract

The city of Yazd has located in central of Iran that experiences dust storms most of the year, and also has witnessed rapid industrial growth, especially in industries with high dust generation capacity (tiles, steel, etc.) during the last two decades. Therefore, predicting the concentration of particulate pollutants during dust storms and industrial pollution through the use of accurate warning systems is critical to maintaining the health of citizens. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and Artificial Neural Network (ANN) models were used at the same time to predict the concentration of PM10 for the next 24 hours and to introduce the most accurate method. For this purpose, were used PM10 data, meteorological parameters, and gas pollutant concentrations of Yazd city from 2015 to 2019 years. The results of this study showed that the ANFIS model with (R2 = 0.989) and accuracy of about 99% were better than other models, followed by the ANN model with (R2 = 0.978) and the SVR model with (R2 = 0.957) had the best accuracy, respectively. Finally, data analysis showed that at the operational scale, city managers can make appropriate and timely decisions based on high-precision predictive models, maintain public health before the start of the PM10 alert status.

Keywords

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Journal of Natural Environment
Volume 74, Issue 1
June 2021
Pages 167-179