تخمین غلظت آلاینده‌های PM2.5 و PM10 هوا با استفاده از داده‌های ماهواره‌ای سنجنده مودیس، شبکه عصبی عمیق و جنگل تصادفی

نوع مقاله : مقاله پژوهشی

نویسنده

دانشکده مهندسی برق و کامپیوتر، دانشگاه تربیت مدرس، تهران، ایران

چکیده

در حالی که بسیاری از مطالعات پیرامون تخمین غلظت آلاینده‌های هوا از جمله ذرات معلق PM2.5 و PM10 از محصولات عمق نوری هواویزهای (AOD) سنجنده‌های ماهواره‌ای استفاده می‌کنند، استفاده از این محصولات به دلیل قدرت تفکیک مکانی پایین برای تهیه نقشه آلودگی شهرهای با وسعت مکانی کم از جمله شهر تهران کارا نیست. جهت حل این موضوع، در این مطالعه به‌طور مستقیم از خود محصولات سطح 1 سنجنده مودیس (و نه محصولات هواویز و آئروسل) آن استفاده شده است. روش پیشنهادی از یک شبکه عصبی عمیق و یک مدل جنگل تصادفی برای تخمین مقادیر غلظت آلاینده‌ها با استفاده از اطلاعات دو باند اول و دوم ماهواره ترا از سنجنده مودیس بهره می‌برد. نتایج ارزیابی حاکی از کارایی قابل قبول روش پیشنهادی در مقایسه با سایر روش‌‌های کارای معرفی شده در سال‌های اخیر است. نتایج این تحقیق منجر به تولید نرم‌افزاری برای تهیه نقشه آلودگی شهر تهران (نقشه غلظت PM2.5 و PM10) با استفاده از تصاویر رایگان سنجنده مودیس شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Concentration Estimation of Air Pollutants (PM2.5 and PM10) Using MODIS Satellite Data, Deep Neural Network and Random Forest

نویسنده [English]

  • Maryam Imani
Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

While many studies estimate air pollutants such as particulate matter (PM), PM2.5 and PM10, have used aerosol optical depth (AOD) products from satellite sensors, utilizing these products for mapping pollution in smaller cities like Tehran is not effective due to their coarse resolution. To address this problem, this study directly uses the Level 1 products of MODIS (instead of aerosol and AOD products). The proposed method employs a deep neural network and a random forest model to estimate the PM values using data from the first two bands of MODIS. The results show the superior performance of the proposed models compared to some state-of-the-art PM estimation methods in recent years. The outcome of this research is the development of a PM map generation software for Tehran (mapping PM2.5 and PM10 concentrations) using freely available MODIS images.

کلیدواژه‌ها [English]

  • Air pollution
  • Particulate matter
  • PM estimation
  • Deep neural network
  • Random forest
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