پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 438 |
| تعداد مقالات | 5,650 |
| تعداد مشاهده مقاله | 7,683,255 |
| تعداد دریافت فایل اصل مقاله | 6,339,731 |
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 | ||
|
[1] K. Yetilmezsoy, B. Ozkaya, M. Cakmakci, Artificial intelligence-based prediction models for environmental engineering, Neural Network World, 21(3) (2011). [2] S.A. Kalogirou, Use of genetic algorithms for the optimal design of flat plate solar collectors, (2003). [3] S. Roy, Prediction of particulate matter concentrations using artificial neural network, Resour. Environ, 2(2) (2012) 30-36. [4] E. Agirre-Basurko, G. Ibarra-Berastegi, I. Madariaga, Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area, Environmental Modelling & Software, 21(4) (2006) 430-446. [5] M. Cakmakci, Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge, Bioprocess and Biosystems Engineering, 30 (2007) 349-357. [6] M.-G. Chun, K.-C. Kwak, J.-W. Ryu, Application of ANFIS for coagulant dosing process in a water purification plant, in: FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No. 99CH36315), IEEE, 1999, pp. 1743-1748. [7] H. Niska, A. Serkkola, Data analytics approach to create waste generation profiles for waste management and collection, Waste Management, 77 (2018) 477-485. [8] N.E. Johnson, O. Ianiuk, D. Cazap, L. Liu, D. Starobin, G. Dobler, M. Ghandehari, Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City, Waste management, 62 (2017) 3-11. [9] C. Estay-Ossandon, A. Mena-Nieto, Modelling the driving forces of the municipal solid waste generation in touristic islands. A case study of the Balearic Islands (2000–2030), Waste management, 75 (2018) 70-81. [10] G. Di Foggia, M. Beccarello, Designing waste management systems to meet circular economy goals: The Italian case, Sustainable Production and Consumption, 26 (2021) 1074-1083. [11] C. Ghinea, E.N. Drăgoi, E.-D. Comăniţă, M. Gavrilescu, T. Câmpean, S. Curteanu, M. Gavrilescu, Forecasting municipal solid waste generation using prognostic tools and regression analysis, Journal of environmental management, 182 (2016) 80-93. [12] J.S. Armstrong, Evaluating forecasting methods, Principles of forecasting: A handbook for researchers and practitioners, (2001) 443-472. [13] R. Noori, M. Abdoli, M.J. Ghazizade, R. Samieifard, Comparison of neural network and principal component-regression analysis to predict the solid waste generation in Tehran, Iranian Journal of Public Health, 38(1) (2009) 74-84. [14] M. Ali Abdoli, M. Falah Nezhad, R. Salehi Sede, S. Behboudian, Longterm forecasting of solid waste generation by the artificial neural networks, Environmental Progress & Sustainable Energy, 31(4) (2012) 628-636. [15] P. Sukholthaman, A. Sharp, A system dynamics model to evaluate effects of source separation of municipal solid waste management: A case of Bangkok, Thailand, Waste management, 52 (2016) 50-61. [16] S. Xiao, H. Dong, Y. Geng, X. Tian, C. Liu, H. Li, Policy impacts on Municipal Solid Waste management in Shanghai: A system dynamics model analysis, Journal of Cleaner Production, 262 (2020) 121366. [17] D. Ju-Long, Control problems of grey systems, Systems & control letters, 1(5) (1982) 288-294. [18] L. Chhay, M.A.H. Reyad, R. Suy, M.R. Islam, M.M. Mian, Municipal solid waste generation in China: Influencing factor analysis and multi-model forecasting, Journal of Material Cycles and Waste Management, 20 (2018) 1761-1770. [19] J.W. Forrester, Industrial dynamics: a major breakthrough for decision makers, Harvard business review, 36(4) (1958) 37-66. [20] B. Dyson, N.-B. Chang, Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling, Waste management, 25(7) (2005) 669-679. [21] N. Kollikkathara, H. Feng, D. Yu, A system dynamic modeling approach for evaluating municipal solid waste generation, landfill capacity and related cost management issues, Waste management, 30(11) (2010) 2194-2203. [22] C. Lee, K. Ng, C. Kwong, S. Tay, A system dynamics model for evaluating food waste management in Hong Kong, China, Journal of Material Cycles and Waste Management, 21 (2019) 433-456. [23] M.A. Babalola, A system dynamics-based approach to help understand the role of food and biodegradable waste management in respect of municipal waste management systems, Sustainability, 11(12) (2019) 3456. [24] T.M. Mak, P.-C. Chen, L. Wang, D.C. Tsang, S. Hsu, C.S. Poon, A system dynamics approach to determine construction waste disposal charge in Hong Kong, Journal of cleaner production, 241 (2019) 118309. [25] Q. Guo, E. Wang, Y. Nie, J. Shen, Profit or environment? A system dynamic model analysis of waste electrical and electronic equipment management system in China, Journal of Cleaner Production, 194 (2018) 34-42. [26] S. Ulli-Beer, D.F. Andersen, G.P. Richardson, Financing a competitive recycling initiative in Switzerland, Ecological economics, 62(3-4) (2007) 727-739. [27] M. Sufian, B. Bala, Modelling of electrical energy recovery from urban solid waste system: The case of Dhaka city, Renewable energy, 31(10) (2006) 1573-1580. [28] D. Inghels, W. Dullaert, An analysis of household waste management policy using system dynamics modelling, Waste management & research, 29(4) (2011) 351-370. [29] A.C.H. Pinha, J.K. Sagawa, A system dynamics modelling approach for municipal solid waste management and financial analysis, Journal of Cleaner Production, 269 (2020) 122350. [30] J. Den Boer, E. Den Boer, J. Jager, LCA-IWM: A decision support tool for sustainability assessment of waste management systems, Waste management, 27(8) (2007) 1032-1045. [31] R. Freeman, L. Jones, M. Yearworth, J.-Y. Cherruault, Systems thinking and system dynamics to support policy making in Defra–project final report, (2014). [32] S. Zhong, K. Zhang, M. Bagheri, J.G. Burken, A. Gu, B. Li, X. Ma, B.L. Marrone, Z.J. Ren, J. Schrier, Machine learning: new ideas and tools in environmental science and engineering, Environmental Science & Technology, 55(19) (2021) 12741-12754. [33] V. Sagan, K.T. Peterson, M. Maimaitijiang, P. Sidike, J. Sloan, B.A. Greeling, S. Maalouf, C. Adams, Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing, Earth-Science Reviews, 205 (2020) 103187. [34] C. Bellinger, M.S. Mohomed Jabbar, O. Zaïane, A. Osornio-Vargas, A systematic review of data mining and machine learning for air pollution epidemiology, BMC public health, 17 (2017) 1-19. [35] Z.M. Yaseen, An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions, Chemosphere, 277 (2021) 130126. [36] A.I. Dounis, C. Caraiscos, Advanced control systems engineering for energy and comfort management in a building environment—A review, Renewable and Sustainable Energy Reviews, 13(6-7) (2009) 1246-1261. [37] X.C. Nguyen, T.P.Q. Tran, T.T.H. Nguyen, D.D. La, V.K. Nguyen, T.P. Nguyen, X. Nguyen, S. Chang, R. Balasubramani, W.J. Chung, Call for planning policy and biotechnology solutions for food waste management and valorization in Vietnam, Biotechnology Reports, 28 (2020) e00529. [38] K.G. Roberts, B.A. Gloy, S. Joseph, N.R. Scott, J. Lehmann, Life cycle assessment of biochar systems: estimating the energetic, economic, and climate change potential, Environmental science & technology, 44(2) (2010) 827-833. [39] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, nature, 521(7553) (2015) 436-444. [40] H. Zhang, H. Cao, Y. Zhou, C. Gu, D. Li, Hybrid deep learning model for accurate classification of solid waste in the society, Urban Climate, 49 (2023) 101485. [41] A. Xu, H. Chang, Y. Xu, R. Li, X. Li, Y. Zhao, Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review, Waste Management, 124 (2021) 385-402. [42] M.T. Munir, B. Li, M. Naqvi, Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions, Fuel, 348 (2023) 128548. [43] O.B. Sara, Mathematical modeling to predict residential solid waste generation, Waste Management, 28 (2008) 7-13. [44] G.Z.M. Jalili, R. Noori, Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad, Int. J. Environ. Res., 2(1) (2008) 13-22. [45] H. Adeli, X. Jiang, Neuro-Fuzzy logic model for Free Way Work Zone Capacity Estimation, Jornal of Transportation Engineering, 129 (2002) 484-493. [46] M. Abbasi, M.N. Rastgoo, B. Nakisa, Monthly and seasonal modeling of municipal waste generation using radial basis function neural network, Environmental Progress & Sustainable Energy, 38(3) (2019) e13033. | ||
|
آمار تعداد مشاهده مقاله: 522 تعداد دریافت فایل اصل مقاله: 440 |
||