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بهبود مدلسازی تولید پسماند شهری با استفاده از یادگیری عمیق و مقایسه با مدلهای هوشمند شبکه عصبی و ماشین بردار پشتیبان | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 12، دوره 57، شماره 2، 1404، صفحه 271-290 اصل مقاله (1.01 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2025.22970.8085 | ||
| نویسندگان | ||
| مریم عباسی* 1؛ سهیل کریمی درمیان2 | ||
| 1دانشکده مهندسی عمران، آب و محیطزیست، دانشگاه شهید بهشتی، تهران، ایران. | ||
| 2دانشکده مهندسی عمران، آب و محیطزیست، دانشگاه شهید بهشتی، تهران، ایران | ||
| چکیده | ||
| هدف از این پژوهش بررسی و مقایسه عملکرد مدلهای هوشمند در مدلسازی کمی پسماند شهری است. ابتدا مولفههای موثر بر تولید پسماند شامل اطلاعات جغرافیایی، اجتماعی، هواشناسی، فرهنگی، اقتصادی بصورت ماهانه و فصلی جمعآوری گردید.. سپس به مدلسازی کمی پسماند شهری در شهر تهران با استفاده از مدلهای هوشمند شبکه عصبی مصنوعی، ماشین بردار پشتیبان و یادگیری عمیق پرداخته شده و نتایج و خطاهای بدست آمده از آنها مورد بررسی قرار گرفته است. طبق مدلسازیهای انجام شده نتیجه گرفته شد؛ مدل رگرسیون و شبکه عصبی مصنوعی کمترین R2 و بیشترین RMSE و MAE را دارند و مدلسازی دقیقی انجام نمیدهند. بر اساس معیارها و خطاهای بدست آمده این نتیجه حاصل شد که هم در دوره ماهانه و هم در دوره فصلی به ترتیب یادگیری عمیق، مدل ماشین بردار پشتیبان، شبکه عصبی مصنوعی و در آخرین رتبه رگرسیون در مدلسازی دقیق عمل کردهاند. مدل ماشین بردار پشتیبان و مدل یادگیری عمیق هم در دوره فصلی و هم در دوره ماهانه کمترین خطاها را در بین مدلهای آزمایش شده دارند. در مدلسازی ماهانه ارقام مشاهده شده به ارقام پیشبینی شده توسط مدل یادگیری عمیق از دیگر مدلها نزدیکترند و تطابق بیشتری دارند، به علاوه مدل یادگیری عمیق در مدلسازی فصلی نیز دقیقتر از دیگر مدلهای آزمایش شده عمل کرده .لازم به ذکراست که الگوریتم یادگیری عمیق در مدلسازی فصلی از مدلسازی ماهانه دقیقتر عمل کرده است؛ زیرا تغییر وزن پسماند بیشتر بهصورت فصلی تغییر میکند و الگوی فصلی را دنبال میکند. طبق منحنی یادگیری نتیجه گرفته شد مدلها در دوره فصلی بهتر عمل میکنند و مقدار پیشبینی شده و مشاهده شده در مدلسازی فصلی بیشتر به هم نزدیک هستند. | ||
| کلیدواژهها | ||
| مدیریت پسماند؛ پسماند شهری؛ مدلسازی؛ یادگیری عمیق؛ نرخ تولید پسماند؛ مدیریت اصولی؛ یادگیری ماشین | ||
| موضوعات | ||
| ضایعات جامد و مواد زائد خطرناک | ||
| عنوان مقاله [English] | ||
| Modeling Municipal Waste Generation Using Support Vector Machine, Artificial Neural Network and Deep Learning | ||
| نویسندگان [English] | ||
| Maryam Abbasi1؛ Soheil Karimi Darmian2 | ||
| 1Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran | ||
| 2Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran | ||
| چکیده [English] | ||
| To evaluate the urban solid waste management program, identifying the factors that affect the production of urban waste plays a vital role. Knowing the factors affecting the production of urban waste and determining the importance of each factor allows the decision-makers to take the necessary measures. The purpose of this research is to investigate the factors affecting waste production, including geographical, social, meteorological, cultural, and economic parameters, and to find their relationship with waste production. Also, finding the factors that have the greatest impact on waste production in Tehran and getting to know them more is one of the goals of this research. In this research, various factors affecting the production of urban waste are identified and the information related to these factors and how they affect the production of waste are evaluated, and the correlation of each of these factors with production waste has been obtained by using Python software and creating a heat map. Then, the quantitative modeling of urban waste in Tehran City using smart regression models, artificial neural networks, support vector machine, and deep learning was discussed and the results and errors obtained from them were analyzed. Using the information sources available in domestic and reliable scientific centers as well as organizations related to this research (related specialized companies, municipalities), available studies in Iran, and some sources and studies available in reliable scientific research sites related to the subject. Abroad, it has been investigated in the field of urban waste. | ||
| کلیدواژهها [English] | ||
| Waste Management, Municipal Waste, Modelling, Deep Learning, Waste Generation, Machine Learning | ||
| مراجع | ||
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