Deep Learning Based a Comprehensive Analysis for Waste Prediction
In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTM-based deep learning method is more successful in the waste prediction problem than the other compared models.
Abbasi M, Abduli M.A., Omidvar B, Baghvand A. (2013). Forecasting Municipal Solid Waste Generation by Hybrid Support Vector Machine and Partial Least Square Model, 7, 27–38. https://doi.org/10.22059/IJER.2012.583.
Abbasi M, El Hanandeh A. (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management, 56,13–22. https://doi.org/10.1016/j.wasman.2016.05.018. DOI: https://doi.org/10.1016/j.wasman.2016.05.018
Adedeji O, Wang Z. (2019). Intelligent waste classification system using deep learning convolutional neural network. Procedia Manuf., 35, 607–612. https://doi.org/10.1016/j.wasman.2016.05.018.
Akanbi LA, Oyedele AO, Oyedele LO, Salami RO. (2020). Deep learning model for Demolition Waste Prediction in a circular economy. J Clean Prod., 274,.122843. https://doi.org/10.1016/j.jclepro.2020.122843.
Ceylan Z. (2020). Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression. Waste Management, 38, 840–850. https://doi.org/10.1177/0734242X20906877.
Coşkun M, Yildirim Ö, Ayşegül U, Demir Y. (2017). An overview of popular deep learning methods. Eur J Tech EJT., 7, 165–176. Retrieved from https://dergipark.org.tr/en/pub/ejt/issue/34562/403498. DOI: https://doi.org/10.23884/ejt.2017.7.2.11
Coskuner G, Jassim MS, Zontul M, Karateke S. (2021). Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes. Waste Management Research, 39 (3), 499–507. https://doi.org/10.1177/0734242X20935181.
Cubillos M. (2020). Multi-site household waste generation forecasting using a deep learning approach. Waste Management, 115, 8–14. https://doi.org/10.1016/j.wasman.2020.06.046.
Daniel Hoornweg, Bhada-Tata-Perinaz. (2012). What a Waste: A Global Review of Solid Waste Management. Washington, District of Columbia: Urban Development & Local Government Unit World Bank.
Fu H, Li Z, Wang R. (2015). Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management, 41, 3–11. https://doi.org/10.1016/j.wasman.2015.03.029. DOI: https://doi.org/10.1016/j.wasman.2015.03.029
Ghanbari F, Kamalan H, Sarraf A. (2021). An evolutionary machine learning approach for municipal solid waste generation estimation utilizing socioeconomic components. Arab J Geoscience, 14(2), 1-16. https://doi.org/10.1007/s12517-020-06348-w.
Huang L, Cai T, Zhu Y, Zhu Y, Wang W, Sun K. (2020). LSTM-Based Forecasting for Urban Construction Waste Generation. Sustainability, 12 (20), 8555. https://doi.org/10.3390/su12208555.
Jahandideh S, Jahandideh S, Asadabadi EB, Askarian M, Movahedi MM, Hosseini S, Jahandideh M. (2009). The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. Waste Management, 29 (11), 2874–2879. https://doi.org/10.1016/j.wasman.2009.06.027. DOI: https://doi.org/10.1016/j.wasman.2009.06.027
Johnson NE, Ianiuk O, Cazap D, Liu L, Starobin D, Dobler G, Ghandehari M (2017) Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City. Waste Management, 62, 3–11. https://doi.org/10.1016/j.wasman.2017.01.037. DOI: https://doi.org/10.1016/j.wasman.2017.01.037
Joshi LM, Bharti RK, Singh R. (2021). Internet of things and machine learning-based approaches in the urban solid waste management: Trends, challenges, and future directions. Expert Systems, 39(5), 12865. https://doi.org/10.1111/exsy.12865.
Kannangara M, Dua R, Ahmadi L, Bensebaa F. (2018). Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management. 74, 3–15. https://doi.org/10.1016/j.wasman.2017.11.057. DOI: https://doi.org/10.1016/j.wasman.2017.11.057
Kaya SK, Yıldırım Ö. (2020). A prediction model for automobiles sales in Turkey using Deep Neural Networks. J Ind Eng. 31, 57–74. Retrieved from https://dergipark.org.tr/en/pub/endustrimuhendisligi/issue/53786/669930.
Kumar A, Samadder SR, Kumar N, Singh C. (2018). Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling. Waste Management, 79, 781–790. https://doi.org/10.1016/j.wasman.2018.08.045.
Lavee D, Khatib M. (2010). Benchmarking in municipal solid waste recycling. Waste Management, 30, 2204–2208. https://doi.org/10.1016/j.wasman.2010.03.032. DOI: https://doi.org/10.1016/j.wasman.2010.03.032
Le X-H, Ho HV, Lee G, Jung S. (2019). Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water, 11 (7), 1387. https://doi.org/10.3390/w11071387.
Maroušek J. (2014). Economically oriented process optimization in waste management. Environment Science Pollution Research, 21 (12), 7400–7402. https://doi.org/10.1007/s11356-014-2688-z. DOI: https://doi.org/10.1007/s11356-014-2688-z
Meza JKS, Yepes DO, Rodrigo-Ilarri J, Cassiraga E. (2019). Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 5(11), e02810. https://doi.org/10.1016/j.heliyon.2019.e02810.
Nguyen XC, Nguyen TTH, La DD, Kumar G, Rene ER, Nguyen DD, Chang SW, Chung WJ, Nguyen XH, Nguyen VK. (2021). Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam. Resource Conserve Recycle, 167,105381. https://doi.org/10.1016/j.resconrec.2020.105381.
Sakr GE, Mokbel M, Darwich A, Khneisser MN, Hadi A. (2016). Comparing deep learning and support vector machines for autonomous waste sorting. In 2016 IEEE international multidisciplinary conference on engineering technology (pp.127-142) .IEEE. 10.1109/IMCET.2016.7777453. DOI: https://doi.org/10.1109/IMCET.2016.7777453
Statista Demographies. In: Statista (2020). https://www.statista.com/statistics/1101883/largest-european-cities/. Accessed 21 April 2021.
Towa E, Zeller V, Achten WM. (2020). Input-output models and waste management analysis: A critical review. Journal of Clean Production, 249, 119359. https://doi.org/10.1016/j.jclepro.2019.119359.
Turan NG, Çoruh S, Akdemir A, Ergun ON. (2009). Municipal solid waste management strategies in Turkey. Waste Management, 29(1), 465-469. https://doi.org/10.1016/j.wasman.2008.06.004. DOI: https://doi.org/10.1016/j.wasman.2008.06.004
Wang C, Qin J, Qu C, Ran X, Liu C, Chen B. (2021). A smart municipal waste management system based on deep-learning and Internet of Things. Waste Management, 135, 20–29. https://doi.org/10.1016/j.wasman.2021.08.028.
TUIK. (2021). https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayal%C4%B1-N%C3%BCfus-Kay%C4%B1t-Sistemi-Sonu%C3%A7lar%C4%B1-2020-37210&dil=1. Accessed 21 April 2021.
Waste Management in Istanbul. (2015). https://atikyonetimi.ibb.istanbul/hizmetlerimiz/kati-atik-aktarim-istasyonlari/. Accessed 30 December 2021.
Where is Istanbul, Turkey on Map Lat Long Coordinates (2021). https://www.latlong.net/place/istanbul-turkey-2242.html. Accessed 21 December 2021.
Wu F, Niu D, Dai S, Wu B. (2020). New insights into regional differences of the predictions of municipal solid waste generation rates using artificial neural networks. Waste Management, 107, 182–190. https://doi.org/10.1016/j.wasman.2020.04.015.
Xu A, Chang H, Xu Y, Li R, Li X, Zhao Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Management, 124, 385–402. https://doi.org/10.1016/j.wasman.2021.02.029 .
Younes MK, Nopiah ZM, Basri NEA, Basri H, Abushammala MFM, Younes MY. (2016). Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model. Waste Management, 55, 3–11. https://doi.org/10.1016/j.wasman.2015.10.020. DOI: https://doi.org/10.1016/j.wasman.2015.10.020