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مجله پژوهش در بهداشت محیط

مجله پژوهش در بهداشت محیط

پیش‌بینی و آنالیز آماری غلظت ذرات با استفاده از هوش مصنوعی مبتنی بر یادگیری ماشین در شهر مشهد

نوع مقاله : مقالات پژوهشی

نویسندگان
احمد مخدومی 1
سمیه ضیائی 2
مریم سرخوش 3
1 دانشجو ارشد مهندسی بهداشت محیط، عضو کمیته تحقیقات دانشجویی دانشکده بهداشت دانشگاه علوم پزشکی مشهد،مشهد، ایران.
2 دانشجو دوره تکمیلی پژوهشی دانشکده بهداشت، عضو کمیته تحقیقات دانشجویی دانشکده بهداشت دانشگاه علوم پزشکی مشهد، مشهد ، ایران.
3 گروه بهداشت محیط، دانشکده بهداشت، دانشگاه علوم پزشکی مشهد، مشهد، ایران
10.22038/jreh.2025.25727
چکیده
زمینه و هدف: آلودگی هوای شهرهای صنعتی و بزرگ ناشی در سال های اخیر عمدتا ناشی از ذرات معلق هستند. پیش‌بینی غلظت آلاینده ها به مدیریت و برنامه‌ریزی صحیح در راستای بهبود کیفیت هوا کمک شایانی خواهد کرد. هدف از این مطالعه پیش‌بینی غلظت ذرات معلق با استفاده از چهار مدل غیرخطی هوش مصنوعی مبتنی بر روش یادگیری ماشین است. 

مواد و روش ها: تکنیک‌های یادگیری ماشین مورد استفاده در این مطالعه شامل: ماشین تقویت گرادیان سبک، رگرسیون تقویت گرادیان پیشرفته، جنگل تصادفی و رگرسیون با تقویت گرادیان بود. داده‌های هواشناسی و غلظت ذرات‌معلق برای پیش‌بینی شاخص کیفیت هوا  جمع‌آوری گردید. 

یافته‌ها: نتایج این مطالعه نشان می دهد مدل جنگل تصادفی بر اساس معیارهای میانگین خطای مطلق و میانگین درصد خطای مطلق، عملکرد بهتری نشان می دهد؛ در صورتیکه مدل رگرسیون با تقویت گرادیان بر اساس معیارهای میانگین خطای مربعات و ریشه میانگین خطای مربع عملکرد قویتری را نشان می دهد. 

نتیجه‌گیری: در نتیجه، این مطالعه روشی را برای به‌دست آوردن نتایج پیش‌بینی  با دقت بالا با استفاده از هوش مصنوعی مبتنی بر یادگیری ماشین را پیشنهاد می‌کند که برای پایش کیفیت هوا در مقیاس جهانی و بهبود ارزیابی مواجهه حاد در تحقیقات اپیدمی مفید است.
کلیدواژه‌ها
آلودگی هوا
‌PM
یادگیری ماشین‌
مدل‌های غیرخطی

عنوان مقاله English

Statistical Analysis and Forecast Modeling of Particles Concentration Using Artificial Intelligence Based on Machine Learning in Mashhad

نویسندگان English

Ahmad Makhdoomi 1
Somayyeh Ziaei 2
Maryam Sarkhosh 3
1 Master's Student in Environmental Health Engineering, Member of the Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
2 Postdoctoral Researcher, School of Health, Member of the Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
3 Department of Environmental Health, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
چکیده English

Background and Purpose: This study aims to forecast PM concentrations using four non-linear Machine Learning (ML) models.

Materials and Methods: The ML techniques employed include Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Regressor (XGBR), Random Forest (RF), and Gradient Boosting Regressor (GBR). Meteorological and pollutant data were collected to predict the Air Quality Index (AQI) in Mashhad, Khorasan Razavi Province, Iran.

Results: Based on the MAE and MAPE metrics, RF demonstrated the best performance, while according to the MSE and RMSE metrics, GBR model was more robust.
Conclusion: This study proposes a high-accuracy PM prediction method using ML, which can be beneficial for global air quality monitoring and improving acute exposure assessments in epidemiological research.
 
Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

کلیدواژه‌ها English

Air quality index
PM
Machine learning
Non-linear Models
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