نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشکده فنی مهندسی، گروه کشاورزی، واحد یزد، دانشگاه آزاد اسلامی یزد، ایران
2 دانشیار محیط زیست، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد،
3 استادیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس
4 دانشیار محیط زیست، دانشکده فنی مهندسی، گروه کشاورزی، واحد یزد، دانشگاه آزاد اسلامی یزد، ایران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
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.
کلیدواژهها [English]
)