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مقایسه الگوریتمهای یادگیری ماشین بهمنظور تخمین غلظت ذرات PM10 با استفاده از شاخص AOD و برخی پارامترهای هواشناسی | ||
تحقیقات آب و خاک ایران | ||
دوره 56، شماره 1، فروردین 1404، صفحه 127-150 اصل مقاله (2.49 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2024.381356.669783 | ||
نویسندگان | ||
فاطمه خدایار1؛ محمد رضا انصاری* 2؛ سعید حجتی3؛ الهام خدایار4 | ||
1کارشناس شهرداری- ملاثانی-ایران | ||
2گروه علوم خاک، دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران | ||
3گروه علوم و مهندسی خاک، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
4گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد اهواز، اهواز، ایران | ||
چکیده | ||
نظارت و کنترل بر میزان و منابع گردوغبار تحت تأثیر تغییرات اقلیمی و توسعه رویکردهای پیشبینی مناسب که تأثیرات مستقیمی بر محیطزیست و سلامت انسان دارد بسیار حائز اهمیت هستند. این مطالعه باهدف تخمین غلظت ذرات کوچکتر از 10 میکرومتر (PM10 ) در شهر اهواز، با استفاده از مدلهای مختلف یادگیری ماشین انجامشده است. از متغیرهای اقلیمی و شاخص عمق بصری (AOD) محصول باند 476 نانومتر سنجنده مودیس بهعنوان متغیرهای مؤثر در برآورد غلظت ذرات PM10 در قالب سه سناریو شامل: ترکیب شاخص AOD با PM10 (سناریو اول)، ترکیب متغیرهای اقلیمی با PM10 (سناریوی دوم) و ترکیب متغیرهای اقلیمی و شاخص AOD با PM10 (سناریوی سوم) استفاده گردید. با استفاده از شش الگوریتم مدل یادگیری ماشین شامل: Random Forest Regression (RFR)، Gradient Boosting Regression (GBR)،(ANN) Artificial Neural Networks، AdaBoostR with DTR، (SVR) Support Vector Regressionو (DTR) Decision Tree Regression، میزان غلظت ذرات (PM10 ) در سناریوهای مختلف با در نظر گرفتن ضرایب صحت و دقت تعیین و مقایسه شدند. مهمترین متغیرهای تأثیرگذار در برآورد میزان PM10: ساعت آفتابی، حداقل دید افقی، ماکزیمم سرعت باد و شاخص AODتعیین گردید. مدل رگرسیون خطی GBR با مقادیر ضرایب R2، MAE، RMSE و IOA به ترتیب برابر با76/0، 31/0، 49/0 و 93/0 مناسبترین مدل در تخمین غلظت ذرات (PM10 ) بوده، که در سناریوی سوم بدست آمد. نتایج نشان داد که استفاده از ترکیب شاخص AODدر کنار متغیرهای اقلیمی منجر به بهبود عملکرد مدل در برآورد غلظت ذرات PM10 شده است. مدل نهائی پیشنهادی میتواند به منظور تخمین روزانه ذرات PM10 استفاده شود. | ||
کلیدواژهها | ||
الگوریتمهای یادگیری ماشین؛ متغیرهای اقلیمی؛ عمق نوری آئروسل؛ ذرات معلق با قطر آئرودینامیکی کمتر از 10 میکرومتر | ||
عنوان مقاله [English] | ||
Comparing machine learning algorithms for estimating PM10 particle concentration using AOD and selected meteorological parameters | ||
نویسندگان [English] | ||
fatemeh Khodayar1؛ mohammadreza ansari2؛ saeid hojati3؛ elham khodayar4 | ||
1Urban Planning Expert mollasani iran. | ||
2Department of Soil Sciences, Faculty of Agriculture, University of Khuzestan Agricultural Sciences and Natural Resources, Mollasani, Iran. | ||
3Department of Soil Science, Shahid Chamran University of Ahvaz, Ahvaz, Khuzestan, Iran | ||
4Department of Software Engineering, Islamic Azad University, Ahvaz Branch, Khuzestan, Iran | ||
چکیده [English] | ||
Monitoring and controlling the level and sources of dust are crucial in the face of climate change and the development of suitable predictive approaches that directly impact the environment and human health. This study aims to estimate the concentration of PM10 in the city of Ahvaz using various machine learning models. Climate variables and the Aerosol Optical Depth (AOD) index, derived from the MODIS sensor at a wavelength of 476 nanometers, were used as influential variables in estimating PM10 concentration in three scenarios: combining AOD with PM10 (scenario 1), combining climate variables with PM10 (scenario 2), and combining climate variables and AOD with PM10 (scenario 3) .Using six machine learning algorithms, namely Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Artificial Neural Networks (ANN), AdaBoostR with DTR, Support Vector Regression (SVR), and Decision Tree Regression (DTR), the PM10 concentration was estimated in different scenarios, considering accuracy and precision coefficients. The most influential variables in estimating PM10 concentration were determined to be sunshine hours, minimum visibility, maximum wind speed, and the AOD index. The GBR linear regression model, with R2, MAE, RMSE, and IOA coefficients of 0.76, 0.31, 0.49 and 0.93 respectively, was found to be the most suitable model for estimating PM10 concentration in scenario 3. the results showed that incorporating the AOD index alongside climate variables improved the model's performance in estimating PM10 concentration. The proposed final model can be used for daily estimation of PM10 particles. | ||
کلیدواژهها [English] | ||
Machine Learning Algorithms, Climatic Variables, AOD index, PM10 | ||
مراجع | ||
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