پیش بینی و مدلسازی غلظت روزانه ذرات معلق (PM2.5 & PM10) زمستانه شهر همدان با شبکه عصبی مصنوعی پرسپترون چند لایه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه محیط‌زیست، دانشکده منابع‌طبیعی و محیط‌زیست، دانشگاه ملایر، ایران

2 دانشجوی دکتری آلودگی محیط‌زیست، دانشکده منابع‌طبیعی و محیط‌زیست، دانشگاه ملایر، ملایر، ایران

10.22034/eiap.2023.169982

چکیده

در سال‌‌های اخیر تعداد روزهای با غلظت بالای ذرات معلق (PM) در شهر همدان بسیار افزایش یافته است. با توجه به شدت بیشتر این پدیده در فصل زمستان، برای مدیریت اثرات بهداشتی و محیط‌زیستی آن در این فصل کوشیده شده است تا با استفاده از شبکه عصبی مصنوعی (ANN) ابزاری بهینه برای پیش‌بینی زود هنگام آن ارایه گردد. برای بررسی عوامل تاثیرگذار بر غلطت PM زمستانه شهر همدان، داده‌های زمستانه آلاینده‌های هوا و پارامترهای هواشناسی با همبستگی پیرسون مورد تحلیل قرار گرفت. سپس بر اساس نتایج حاصل، شبکه عصبی پرسپترون چندلایه(MLP-ANN) بهینه شده بر اساس آزمون و آزمایش مدل‌سازی و مقادیر PM پیش‌بینی شد. در بین عوامل هواشناسی و کیفی هوا، متغیرهای کیفی هوا دارای همبستگی بیشتری با غلظت PM زمستانه بودند. ANN در مدلی با 3 لایه ورودی، 1 لایه پنهان و 4 پردازنده لایه میانی با دقت بالای 90% میزان PM2.5 و مدلی با یک لایه ورودی، یک لایه پنهان و 5 پردازنده لایه میانی با دقتی بالای 90% میزان PM10 را پیش‌بینی نمود. باوجود قرارگیری کوه الوند بین باد غالب نواحی غربی کشور و شهر همدان تاثیر عوامل هواشناسی بر غلظت PM کم می‌‌باشد. همچنین افزایش PM زمستانه شهر می‌‌تواند ناشی افزایش مصرف سوخت و تولید آلاینده‌‌های حاصل از احتراق در زمستان باشد. ابزار MLP-ANN با کمترین و دردسترس ترین داده‌‌ها دارای قابلیت پیش‌بینی زود هنگام میزان PM می‌‌باشد و می‌‌توان از آن برای کنترل اثرات PM بهره گرفت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Predicting and Modeling of Daily Concentration of Particulate Matter (PM2.5 & PM10) in Hamadan Winter with Multilayer Perceptron Neural Network

نویسندگان [English]

  • Eisa Solgi 1
  • Mohamad Parsi Mehr 2
1 Associate Professor of Department of Environment, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran
2 Ph.D student ‎of Environmental Pollution, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran
چکیده [English]

In recent years, the number of days with high concentrations of particulate matter (PM) has been increased in Hamadan city. Since this phenomenon is more prevalent in winter, in this research, an optimal artificial neural network model has been proposed to predict the concentration of PM in winter. To investigate the concentration of winter PM in Hamedan, the winter data of air pollutants and meteorological parameters were analyzed with Pearson correlation. Then, according to the results, Multilayer Perceptron Artificial Neural Network (MLP-ANN) with an optimized structure based on the training and testing was used to predict the daily concentration of PM2.5 & PM10. Among meteorological and air quality factors, air quality variables were more correlated with winter PM concentration. MLP-ANN predicted PM2.5 in a model with 3 input layers, 1 hidden layer and 4 middle layer processors with R: 0.93 and also with 1 input layer, 1 hidden layer and 5 middle layer processors with R: 0.92. Predicted PM10. Alvand Mountains are located between the prevailing wind in the western parts of the Iran and Hamedan, therefore the effect of meteorological factors on PM concentration is low. Also, the increase of winter PM in the city can be due to increased fuel consumption in winter. The MLP-ANN tool with the lowest and most accessible data has the ability to predict PM early and can be used to control PM effects.
 

کلیدواژه‌ها [English]

  • Artificial Intelligence
  • Environmental Assessment
  • Environmental Pollution
  • Air Pollution
  • Hamedan
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