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مقایسه دقت مدل رگرسیون بردار پشتیبان با دو روش رایج شبکه عصبی مصنوعی و شبکه عصبی فازی- تطبیقی در پیش بینی غلظت آلاینده PM10 | ||
| نشریه محیط زیست طبیعی | ||
| دوره 74، شماره 1، خرداد 1400، صفحه 167-179 اصل مقاله (3.8 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jne.2021.307289.2041 | ||
| نویسندگان | ||
| سید مصطفی میرزاده1؛ فرهاد نژاد کورکی* 2؛ وحید موسوی3؛ سید ابوالقاسم میرحسینی ده آبادی4 | ||
| 1دانشکده فنی مهندسی، گروه کشاورزی، واحد یزد، دانشگاه آزاد اسلامی یزد، ایران | ||
| 2دانشیار محیط زیست، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، | ||
| 3استادیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس | ||
| 4دانشیار محیط زیست، دانشکده فنی مهندسی، گروه کشاورزی، واحد یزد، دانشگاه آزاد اسلامی یزد، ایران | ||
| چکیده | ||
| شهر یزد واقع در مرکز ایران می باشد که در اکثر ایام سال طوفان های گرد و غبار را تجربه می کند، و همچنین طی دو دهه گذشته شاهد رشد سریع صنعتی بخصوص در صنایع با توان غبار زایی بالا (کاشی، فولاد و ..) بوده است، لذا پیش بینی غلظت آلاینده ذرات از طریق بکارگیری سیستم های پیش آگاهی دقیق در زمان طوفان های گرد و غبار و آلودگی های صنعتی برای حفظ بهداشت و سلامت شهروندان بسیار حیاتی است. در این مطالعه بطور همزمان از مدل شبکه عصبی فازی تطبیقی (ANFIS)، رگرسیون بردار پشتیبان (SVR) و شبکه عصبی مصنوعی (ANN) در پیش بینی میزان غلظت آلاینده PM10 برای 24 ساعت آینده و معرفی دقیق ترین روش استفاده شد. به همین منظور داده های PM10 شهر یزد به همراه پارامتر هواشناسی شهر در بازه زمانی سال های 1394 تا 1398 مورد استفاده قرار گرفت. نتایج این تحقیق نشان داد که مدل ANFIS با (R2=0.989) و دقتی در حدود 99 درصدی بهتر از سایر مدل ها در این حالت است و بعد از آن به ترتیب مدل ANN با (R2=0.978) و SVR با (R2=0.957) دارای بهترین دقت بودند. بنابراین می توان گفت، با توجه به صحت مدل ، از این مدل می توان برای پیش بینی غلظت آلاینده PM10 استفاده کرد و این امر می تواند مسئولین را در تصمیم گیری های به موقع در جهت حفظ سلامت عمومی قبل از شروع وضعیت هشدار غلظت آلاینده PM10 کمک کند. | ||
| کلیدواژه‌ها | ||
| PM10؛ پیش بینی 24 ساعته؛ شبکه عصبی فازی تطبیقی | ||
| عنوان مقاله [English] | ||
| 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 | ||
| نویسندگان [English] | ||
| Seyyed Mostafa Mirzadeh1؛ Farhad Nejadkoorki2؛ Vahid Moosavi3؛ Seyyed Abolghasem Mirhoseini4 | ||
| 1Department of Agriculture, College of Engineering, Yazd Branch, Yazd Islamic Azad University, Yazd, Iran | ||
| 2Associate Professor of Environmental Science, School of Natural Resources and Desert Studies, Yazd University. | ||
| 3Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran | ||
| 4Department of Agriculture, College of Engineering, Yazd Branch, Yazd Islamic Azad University, Yazd, Iran | ||
| چکیده [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] | ||
| PM10, 24-hour prediction, Adaptive Neuro-Fuzzy Inference System | ||
| مراجع | ||
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