Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions

Authors

DOI:

https://doi.org/10.62411/faith.3048-3719-85

Keywords:

Artificial Intelligence, Indonesia, Internet of Things (IoT), Sustainable Waste Management, Waste Management

Abstract

Indonesia’s waste management system struggles to keep pace with rapid population growth and urbanization, resulting in inefficient waste collection, environmental degradation, and low recycling rates. The country predominantly relies on open dumping and landfilling, which contribute significantly to pollution and greenhouse gas emissions. This study explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in waste management, focusing on smart waste collection, automated sorting, real-time monitoring, and predictive analytics. AI-driven waste classification enhances recycling efficiency, while IoT-enabled smart bins optimize collection routes, reducing operational costs and landfill dependency. Despite these advantages, challenges such as high implementation costs, digital infrastructure limitations, and data privacy concerns hinder widespread adoption. This study highlights that policy support, investment in digital infrastructure, and stakeholder collaboration are crucial for successful implementation. By leveraging AI and IoT, Indonesia can significantly improve waste management efficiency, minimize environmental impact, and advance circular economy initiatives. The findings suggest that, with the right policies and investments, AI-driven waste management can drive sustainability, reduce waste mismanagement, and promote resource optimization, making it a vital strategy for future urban development in Indonesia.

 

Downloads

Download data is not yet available.

Author Biographies

Sameh Fuqaha, Universitas Muhammadiyah Yogyakarta

Masters of Civil Engineering, Postgraduate Studies, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia

Nursetiawan Nursetiawan, Universitas Muhammadiyah Yogyakarta

Department of Civil Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia

References

BPS - Statistics Indonesia, “Mid Year Population - Statistical Data - BPS - Statistics Indonesia,” BPS - Statistics Indonesia, 2024. https://www.bps.go.id/en/statistics-table/2/MTk3NSMy/mid-year-population--thousand-people-.html (accessed Mar. 10, 2025).

Kementerian Lingkungan Hidup dan Kehutanan, “SIPSN - Sistem Informasi Pengelolaan Sampah Nasional,” Sistem Informasi Pengelolaan Sampah Nasional, 2023. https://sipsn.menlhk.go.id/sipsn/

P. Damayanti, S. S. Moersidik, and J. T. Haryanto, “Waste to Energy in Sunter, Jakarta, Indonesia: Plans and Challenges,” IOP Conf. Ser. Earth Environ. Sci., vol. 940, no. 1, p. 012033, Dec. 2021, doi: 10.1088/1755-1315/940/1/012033.

E. A. Wikurendra, A. Csonka, I. Nagy, and G. Nurika, “Urbanization and Benefit of Integration Circular Economy into Waste Management in Indonesia: A Review,” Circular Economy and Sustainability, vol. 4, no. 2. 2024. doi: 10.1007/s43615-024-00346-w.

F. Cucchiella, I. D’Adamo, and M. Gastaldi, “Sustainable waste management: Waste to energy plant as an alternative to landfill,” Energy Convers. Manag., vol. 131, pp. 18–31, Jan. 2017, doi: 10.1016/j.enconman.2016.11.012.

R. Verma, K. S. Vinoda, M. Papireddy, and A. N. S. Gowda, “Toxic Pollutants from Plastic Waste- A Review,” Procedia Environ. Sci., vol. 35, pp. 701–708, 2016, doi: 10.1016/j.proenv.2016.07.069.

S. Kaza, L. C. Yao, P. Bhada-Tata, and F. Van Woerden, What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. Washington, DC: World Bank, 2018. doi: 10.1596/978-1-4648-1329-0.

X. Chew, K. W. Khaw, A. Alnoor, M. Ferasso, H. Al Halbusi, and Y. R. Muhsen, “Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach,” Environ. Sci. Pollut. Res., vol. 30, no. 21, pp. 60473–60499, Apr. 2023, doi: 10.1007/s11356-023-26677-z.

E. Karbassiyazdi et al., “XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions,” Environ. Res., vol. 215, p. 114286, Dec. 2022, doi: 10.1016/j.envres.2022.114286.

A. Maiurova et al., “Promoting digital transformation in waste collection service and waste recycling in Moscow (Russia): Applying a circular economy paradigm to mitigate climate change impacts on the environment,” J. Clean. Prod., vol. 354, no. 131604, p. 131604, Jun. 2022, doi: 10.1016/j.jclepro.2022.131604.

N. Kumari, S. Pandey, A. K. Pandey, and M. Banerjee, “Role of Artificial Intelligence in Municipal Solid Waste Management,” Br. J. Multidiscip. Adv. Stud., vol. 4, no. 3, pp. 5–13, May 2023, doi: 10.37745/bjmas.2022.0180.

R. Aniza, W.-H. Chen, A. Pétrissans, A. T. Hoang, V. Ashokkumar, and M. Pétrissans, “A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach,” Environ. Pollut., vol. 324, no. 121363, p. 121363, May 2023, doi: 10.1016/j.envpol.2023.121363.

X. Hu, Y. Zhou, S. Vanhullebusch, R. Mestdagh, Z. Cui, and J. Li, “Smart building demolition and waste management frame with image-to-BIM,” J. Build. Eng., vol. 49, no. 104058, p. 104058, May 2022, doi: 10.1016/j.jobe.2022.104058.

A. Di Vaio, R. Hassan, G. D’Amore, and A. Dello Strologo, “Digital Technologies for Sustainable Waste Management On-Board Ships: An Analysis of Best Practices From the Cruise Industry,” IEEE Trans. Eng. Manag., vol. 71, pp. 12715–12728, 2024, doi: 10.1109/TEM.2022.3197241.

K. H. Yu, Y. Zhang, D. Li, C. E. Montenegro-Marin, and P. M. Kumar, “Environmental planning based on reduce, reuse, recycle and recover using artificial intelligence,” Environ. Impact Assess. Rev., vol. 86, no. 106492, p. 106492, Jan. 2021, doi: 10.1016/j.eiar.2020.106492.

M. Ghahramani, M. Zhou, A. Molter, and F. Pilla, “IoT-Based Route Recommendation for an Intelligent Waste Management System,” IEEE Internet Things J., vol. 9, no. 14, pp. 11883–11892, Jul. 2022, doi: 10.1109/JIOT.2021.3132126.

C. M. Annur, “Sampah Indonesia Bertambah pada 2022, Terbanyak dalam Empat Tahun,” Databoks.Katadata.co.id, 2023. https://databoks.katadata.co.id/lingkungan/statistik/7e4ba334b733220/sampah-indonesia-bertambah-pada-2022-terbanyak-dalam-empat-tahun

H.-N. Guo, S.-B. Wu, Y.-J. Tian, J. Zhang, and H.-T. Liu, “Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review,” Bioresour. Technol., vol. 319, no. 124114, p. 124114, Jan. 2021, doi: 10.1016/j.biortech.2020.124114.

M. Zhang and J. Yan, “A data-driven method for optimizing the energy consumption of industrial robots,” J. Clean. Prod., vol. 285, no. 124862, p. 124862, Feb. 2021, doi: 10.1016/j.jclepro.2020.124862.

Z. Dong, J. Chen, and W. Lu, “Computer vision to recognize construction waste compositions: A novel boundary-aware transformer (BAT) model,” J. Environ. Manage., vol. 305, no. 114405, p. 114405, Mar. 2022, doi: 10.1016/j.jenvman.2021.114405.

S. Majchrowska et al., “Deep learning-based waste detection in natural and urban environments,” Waste Manag., vol. 138, pp. 274–284, Feb. 2022, doi: 10.1016/j.wasman.2021.12.001.

S. Sundaralingam and N. Ramanathan, “A Deep Learning-Based approach to Segregate Solid Waste Generated in Residential Areas,” Eng. Technol. Appl. Sci. Res., vol. 13, no. 2, pp. 10439–10446, Apr. 2023, doi: 10.48084/etasr.5716.

T.-W. Wu, H. Zhang, W. Peng, F. Lü, and P.-J. He, “Applications of convolutional neural networks for intelligent waste identification and recycling: A review,” Resour. Conserv. Recycl., vol. 190, p. 106813, Mar. 2023, doi: 10.1016/j.resconrec.2022.106813.

S. K. Khanal, A. Tarafdar, and S. You, “Artificial intelligence and machine learning for smart bioprocesses,” Bioresour. Technol., vol. 375, p. 128826, May 2023, doi: 10.1016/j.biortech.2023.128826.

F. Ghanbari, H. Kamalan, and A. Sarraf, “Predicting solid waste generation based on the ensemble artificial intelligence models under uncertainty analysis,” J. Mater. Cycles Waste Manag., vol. 25, no. 2, pp. 920–930, Mar. 2023, doi: 10.1007/s10163-023-01589-9.

M. Rosecký, R. Šomplák, J. Slavík, J. Kalina, G. Bulková, and J. Bednář, “Predictive modelling as a tool for effective municipal waste management policy at different territorial levels,” J. Environ. Manage., vol. 291, no. 112584, p. 112584, Aug. 2021, doi: 10.1016/j.jenvman.2021.112584.

Sunayana, S. Kumar, and R. Kumar, “Forecasting of municipal solid waste generation using non-linear autoregressive (NAR) neural models,” Waste Manag., vol. 121, pp. 206–214, Feb. 2021, doi: 10.1016/j.wasman.2020.12.011.

S. Dodampegama, L. Hou, E. Asadi, G. Zhang, and S. Setunge, “Revolutionizing construction and demolition waste sorting: Insights from artificial intelligence and robotic applications,” Resour. Conserv. Recycl., vol. 202, p. 107375, Mar. 2024, doi: 10.1016/j.resconrec.2023.107375.

P. Sharma and U. Vaid, “Emerging role of artificial intelligence in waste management practices,” IOP Conf. Ser. Earth Environ. Sci., vol. 889, no. 1, p. 012047, Nov. 2021, doi: 10.1088/1755-1315/889/1/012047.

T. D. T. Oyedotun and S. Moonsammy, “Linking national policies to beneficiaries: Geospatial and statistical focus to waste and sanitation planning,” Environ. Challenges, vol. 4, no. 100142, p. 100142, Aug. 2021, doi: 10.1016/j.envc.2021.100142.

P. Delanoë, D. Tchuente, and G. Colin, “Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions,” J. Environ. Manage., vol. 331, no. 117261, p. 117261, Apr. 2023, doi: 10.1016/j.jenvman.2023.117261.

H. M. K. K. M. B. Herath and M. Mittal, “Adoption of artificial intelligence in smart cities: A comprehensive review,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 1, p. 100076, Apr. 2022, doi: 10.1016/j.jjimei.2022.100076.

T. A. Kurniawan et al., “Transformation of Solid Waste Management in China: Moving towards Sustainability through Digitalization-Based Circular Economy,” Sustainability, vol. 14, no. 4, p. 2374, Feb. 2022, doi: 10.3390/su14042374.

I. Ihsanullah, G. Alam, A. Jamal, and F. Shaik, “Recent advances in applications of artificial intelligence in solid waste management: A review,” Chemosphere, vol. 309, p. 136631, Dec. 2022, doi: 10.1016/j.chemosphere.2022.136631.

S. Keerthana, B. Kiruthika, R. Lokeshvaran, B. Midhunchakkaravarthi, and G. Dhivyasri, “A Review on Smart Waste Collection and Disposal System,” J. Phys. Conf. Ser., vol. 1969, no. 1, p. 012029, Jul. 2021, doi: 10.1088/1742-6596/1969/1/012029.

K. Ahmed, M. Kumar Dubey, A. Kumar, and S. Dubey, “Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review,” Meas. Sensors, vol. 36, no. 101395, p. 101395, Dec. 2024, doi: 10.1016/j.measen.2024.101395.

J. Chen, S. Huang, S. BalaMurugan, and G. S. Tamizharasi, “Artificial intelligence based e-waste management for environmental planning,” Environ. Impact Assess. Rev., vol. 87, no. 106498, p. 106498, Mar. 2021, doi: 10.1016/j.eiar.2020.106498.

F. Lanzalonga, R. Marseglia, A. Irace, and P. Pietro Biancone, “The application of artificial intelligence in waste management: understanding the potential of data-driven approaches for the circular economy paradigm,” Manag. Decis., Feb. 2024, doi: 10.1108/MD-10-2023-1733.

M. Abdallah, M. Abu Talib, S. Feroz, Q. Nasir, H. Abdalla, and B. Mahfood, “Artificial intelligence applications in solid waste management: A systematic research review,” Waste Manag., vol. 109, pp. 231–246, May 2020, doi: 10.1016/j.wasman.2020.04.057.

T. Yigitcanlar et al., “Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?,” J. Open Innov. Technol. Mark. Complex., vol. 6, no. 4, p. 187, Dec. 2020, doi: 10.3390/joitmc6040187.

Fortune Business Insights, “Artificial Intelligence Market Size, Share & Forcast 2030,” 2023. https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114 (accessed Mar. 10, 2025).

O. Adeleke, S. Akinlabi, T.-C. Jen, and I. Dunmade, “A machine learning approach for investigating the impact of seasonal variation on physical composition of municipal solid waste,” J. Reliab. Intell. Environ., vol. 9, no. 2, pp. 99–118, Jun. 2023, doi: 10.1007/s40860-021-00168-9.

L. Huang, T. Song, and T. Jiang, “Linear regression combined KNN algorithm to identify latent defects for imbalance data of ICs,” Microelectronics J., vol. 131, p. 105641, Jan. 2023, doi: 10.1016/j.mejo.2022.105641.

M. Abdulredha, R. Al Khaddar, D. Jordan, P. Kot, A. Abdulridha, and K. Hashim, “Estimating solid waste generation by hospitality industry during major festivals: A quantification model based on multiple regression,” Waste Manag., vol. 77, pp. 388–400, Jul. 2018, doi: 10.1016/j.wasman.2018.04.025.

S. Golbaz, R. Nabizadeh, and H. S. Sajadi, “Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence,” J. Environ. Heal. Sci. Eng., vol. 17, no. 1, pp. 41–51, Jun. 2019, doi: 10.1007/s40201-018-00324-z.

J. A. Ferreira, M. C. Figueiredo, and J. A. Oliveira, “Household Packaging Waste Management,” in Computational Science and Its Applications – ICCSA 2017, Cham: Springer International Publishing, 2017, pp. 611–620. doi: 10.1007/978-3-319-62395-5_42.

E. Wikurendra, A. Syafiuddin, N. Herdiani, and G. Nurika, “Forecast of Waste Generated and Waste Fleet using Linear Regression Model,” Polish J. Environ. Stud., vol. 32, no. 2, pp. 1867–1876, Mar. 2023, doi: 10.15244/pjoes/158779.

R. Gholami and N. Fakhari, “Support Vector Machine: Principles, Parameters, and Applications,” in Handbook of Neural Computation, Elsevier, 2017, pp. 515–535. doi: 10.1016/B978-0-12-811318-9.00027-2.

J. K. Solano Meza, D. Orjuela Yepes, J. Rodrigo-Ilarri, and E. Cassiraga, “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, vol. 5, no. 11, p. e02810, Nov. 2019, doi: 10.1016/j.heliyon.2019.e02810.

T. Abunama, F. Othman, M. Ansari, and A. El-Shafie, “Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill,” Environ. Sci. Pollut. Res., vol. 26, no. 4, pp. 3368–3381, Feb. 2019, doi: 10.1007/s11356-018-3749-5.

F. G. Altin, İ. Budak, and F. Özcan, “Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey,” Sustain. Chem. Pharm., vol. 33, no. 101060, p. 101060, Jun. 2023, doi: 10.1016/j.scp.2023.101060.

O. O. Ayeleru, L. I. Fajimi, B. O. Oboirien, and P. A. Olubambi, “Forecasting municipal solid waste quantity using artificial neural network and supported vector machine techniques: A case study of Johannesburg, South Africa,” J. Clean. Prod., vol. 289, no. 125671, p. 125671, Mar. 2021, doi: 10.1016/j.jclepro.2020.125671.

G.-W. Cha, H.-J. Moon, and Y.-C. Kim, “Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables,” Int. J. Environ. Res. Public Health, vol. 18, no. 16, p. 8530, Aug. 2021, doi: 10.3390/ijerph18168530.

M. Graus, P. Niemietz, M. T. Rahman, M. Hiller, and M. Pahlenkemper, “Machine learning approach to integrate waste management companies in micro grids,” in 2018 19th International Scientific Conference on Electric Power Engineering (EPE), May 2018, pp. 1–6. doi: 10.1109/EPE.2018.8396029.

A. Kumar, S. R. Samadder, N. Kumar, and C. Singh, “Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling,” Waste Manag., vol. 79, pp. 781–790, Sep. 2018, doi: 10.1016/j.wasman.2018.08.045.

V. Sousa, I. Meireles, V. Oliveira, and C. Dias-Ferreira, “Prediction Performance of Separate Collection of Packaging Waste Yields Using Genetic Algorithm Optimized Support Vector Machines,” Waste and Biomass Valorization, vol. 10, no. 12, pp. 3603–3612, Dec. 2019, doi: 10.1007/s12649-019-00656-3.

F. D’Morison, C. Bittencourt, and L. Ferraz, “Bin level detection based on wall entropy perturbation in electronic waste collection,” in Proceedings of the World Congress on Engineering and Computer Science 2013, 2013, vol. 1. [Online]. Available: https://www.iaeng.org/publication/WCECS2013/WCECS2013_pp52-56.pdf

T. Singh and R. V. S. Uppaluri, “Machine learning tool-based prediction and forecasting of municipal solid waste generation rate: a case study in Guwahati, Assam, India,” Int. J. Environ. Sci. Technol., vol. 20, no. 11, pp. 12207–12230, Nov. 2023, doi: 10.1007/s13762-022-04644-4.

M. Zarei, M. R. Bayati, M. Ebrahimi-Nik, A. Rohani, and B. Hejazi, “Modelling the removal efficiency of hydrogen sulfide from biogas in a biofilter using multiple linear regression and support vector machines,” J. Clean. Prod., vol. 404, no. 136965, p. 136965, Jun. 2023, doi: 10.1016/j.jclepro.2023.136965.

L. Zhu, Z. Tian, and J. Du, “Spatial–temporal redundancy evaluation of the municipal solid waste incineration treatment capacity: the case study of China,” Environ. Sci. Pollut. Res., vol. 31, no. 7, pp. 9948–9963, Apr. 2023, doi: 10.1007/s11356-023-26989-0.

C. Srinilta and S. Kanharattanachai, “Municipal Solid Waste Segregation with CNN,” in 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), Jul. 2019, pp. 1–4. doi: 10.1109/ICEAST.2019.8802522.

V. G. Costa and C. E. Pedreira, “Recent advances in decision trees: an updated survey,” Artif. Intell. Rev., vol. 56, no. 5, pp. 4765–4800, Jul. 2023, doi: 10.1007/s10462-022-10275-5.

A. Gulghane, R. L. Sharma, and P. Borkar, “A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach,” Asian J. Civ. Eng., vol. 25, no. 1, pp. 265–280, Jan. 2024, doi: 10.1007/s42107-023-00772-5.

W. Lu, “Big data analytics to identify illegal construction waste dumping: A Hong Kong study,” Resour. Conserv. Recycl., vol. 141, pp. 264–272, Feb. 2019, doi: 10.1016/j.resconrec.2018.10.039.

M. A. Massoud, C. Abdallah, F. Merhbi, R. Khoury, and R. Ghanem, “Development and application of a prioritization and rehabilitation decision support tool for uncontrolled waste disposal sites in developing countries,” Integr. Environ. Assess. Manag., vol. 19, no. 2, pp. 436–445, Mar. 2023, doi: 10.1002/ieam.4665.

N. Kanwisher, M. Khosla, and K. Dobs, “Using artificial neural networks to ask ‘why’ questions of minds and brains,” Trends Neurosci., vol. 46, no. 3, pp. 240–254, Mar. 2023, doi: 10.1016/j.tins.2022.12.008.

G. Coskuner, M. S. Jassim, M. Zontul, and S. Karateke, “Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes,” Waste Manag. Res. J. a Sustain. Circ. Econ., vol. 39, no. 3, pp. 499–507, Mar. 2021, doi: 10.1177/0734242X20935181.

E. Puntarić, L. Pezo, Ž. Zgorelec, J. Gunjača, D. Kučić Grgić, and N. Voća, “Prediction of the Production of Separated Municipal Solid Waste by Artificial Neural Networks in Croatia and the European Union,” Sustainability, vol. 14, no. 16, p. 10133, Aug. 2022, doi: 10.3390/su141610133.

C. Magazzino, M. Mele, N. Schneider, and S. A. Sarkodie, “Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy?,” Sci. Total Environ., vol. 755, p. 142510, Feb. 2021, doi: 10.1016/j.scitotenv.2020.142510.

B. Ribic, L. Pezo, D. Sincic, B. Loncar, and N. Voca, “Predictive model for municipal waste generation using artificial neural networks—Case study City of Zagreb, Croatia,” Int. J. Energy Res., vol. 43, no. 11, pp. 5701–5713, Sep. 2019, doi: 10.1002/er.4632.

M. S. Jassim, G. Coskuner, N. Sultana, and S. M. Z. Hossain, “Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network,” Appl. Soft Comput., vol. 133, p. 109908, Jan. 2023, doi: 10.1016/j.asoc.2022.109908.

O. Adedeji and Z. Wang, “Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network,” Procedia Manuf., vol. 35, pp. 607–612, 2019, doi: 10.1016/j.promfg.2019.05.086.

U. Soni, A. Roy, A. Verma, and V. Jain, “Forecasting municipal solid waste generation using artificial intelligence models—a case study in India,” SN Appl. Sci., vol. 1, no. 2, p. 162, Feb. 2019, doi: 10.1007/s42452-018-0157-x.

D. O. Melinte, A.-M. Travediu, and D. N. Dumitriu, “Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification,” Appl. Sci., vol. 10, no. 20, p. 7301, Oct. 2020, doi: 10.3390/app10207301.

M. A. Mohammed et al., “Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities,” Multimed. Tools Appl., vol. 82, no. 25, pp. 39617–39632, Oct. 2023, doi: 10.1007/s11042-021-11537-0.

Y. Lu et al., “Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model,” Chemosphere, vol. 318, p. 137708, Mar. 2023, doi: 10.1016/j.chemosphere.2022.137708.

A. Singh, “Solid waste management through the applications of mathematical models,” Resour. Conserv. Recycl., vol. 151, p. 104503, Dec. 2019, doi: 10.1016/j.resconrec.2019.104503.

M. S. Nafiz, S. S. Das, M. K. Morol, A. Al Juabir, and D. Nandi, “ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning,” in 2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Jan. 2023, vol. 2023-Janua, pp. 181–186. doi: 10.1109/ICREST57604.2023.10070078.

F. M. Assef, M. T. A. Steiner, and E. P. de Lima, “A review of clustering techniques for waste management,” Heliyon, vol. 8, no. 1, p. e08784, Jan. 2022, doi: 10.1016/j.heliyon.2022.e08784.

M. M. Kaya et al., “Designing a Smart Home Management System with Artificial Intelligence & Machine Learning,” ResearchGate. 2021. doi: 10.13140/RG.2.2.33082.72641/1.

C. Martin-Rios, A. Hofmann, and N. Mackenzie, “Sustainability-Oriented Innovations in Food Waste Management Technology,” Sustainability, vol. 13, no. 1, p. 210, Dec. 2020, doi: 10.3390/su13010210.

E. J. Adwan, N. Mohamed, H. Bureshaid, and B. Mohamed, “A Mobile App Development for E-Waste ‎Management in Bahrain (Athar)‎‎,” J. Comput. Theor. Appl., vol. 2, no. 2, pp. 164–181, Aug. 2024, doi: 10.62411/jcta.10930.

K. Gupta, A. K. Kar, and M. P. Gupta, “Internet of Things and Sustainability: A Literature Review,” in IFIP Advances in Information and Communication Technology, vol. 699 AICT, 2024, pp. 35–45. doi: 10.1007/978-3-031-50204-0_4.

A. Xu, H. Chang, Y. Xu, R. Li, X. Li, and Y. Zhao, “Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review,” Waste Manag., vol. 124, pp. 385–402, Apr. 2021, doi: 10.1016/j.wasman.2021.02.029.

A. A. Noman, U. H. Akter, T. H. Pranto, and A. B. Haque, “Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review,” Ann. Emerg. Technol. Comput., vol. 6, no. 2, pp. 13–40, Apr. 2022, doi: 10.33166/AETiC.2022.02.002.

Zamathula Queen Sikhakhane Nwokediegwu, Ejike David Ugwuanyi, Michael Ayorinde Dada, Michael Tega Majemite, and Alexander Obaigbena, “AI-Driven Waste Management Systems: A Comparative Review of Innovations in The USA And Africa,” Eng. Sci. Technol. J., vol. 5, no. 2, pp. 507–516, Feb. 2024, doi: 10.51594/estj.v5i2.828.

A. E. Maragkaki, G. Sabathianakis, G. Litas, A. Poda, C. Tsompanidis, and T. Manios, “Life cycle assessment of source separation of biowaste, pay as you throw systems and autonomous composting units in the Municipality of Katerini, Greece,” J. Mater. Cycles Waste Manag., vol. 25, no. 4, pp. 2498–2512, Jul. 2023, doi: 10.1007/s10163-023-01708-6.

G. Messina, A. Tomasi, G. Ivaldi, and F. Vidoli, “‘Pay as you own’ or ‘pay as you throw’? A counterfactual evaluation of alternative financing schemes for waste services,” J. Clean. Prod., vol. 412, p. 137363, Aug. 2023, doi: 10.1016/j.jclepro.2023.137363.

B. Bilitewski, “From traditional to modern fee systems,” Waste Manag., vol. 28, no. 12, pp. 2760–2766, Dec. 2008, doi: 10.1016/j.wasman.2008.03.032.

A. Okubanjo, O. B. Olufemi, A. Okandeji, and E. Daniel, “Smart Bin and IoT: A Sustainable Future for Waste Management System in Nigeria,” Gazi Univ. J. Sci., vol. 37, no. 1, pp. 222–235, Mar. 2024, doi: 10.35378/gujs.1254271.

S. Dubey, M. K. Singh, P. Singh, and S. Aggarwal, “Waste Management of Residential Society using Machine Learning and IoT Approach,” in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Mar. 2020, pp. 293–297. doi: 10.1109/ESCI48226.2020.9167526.

S. Dubey, P. Singh, P. Yadav, and K. K. Singh, “Household Waste Management System Using IoT and Machine Learning,” Procedia Comput. Sci., vol. 167, pp. 1950–1959, 2020, doi: 10.1016/j.procs.2020.03.222.

J. C. B. F. Bijos, V. M. Zanta, J. Morató, L. M. Queiroz, and K. P. S. R. Oliveira-Esquerre, “Improving circularity in municipal solid waste management through machine learning in Latin America and the Caribbean,” Sustain. Chem. Pharm., vol. 28, p. 100740, Sep. 2022, doi: 10.1016/j.scp.2022.100740.

M. Rodrigues, V. Miguéis, S. Freitas, and T. Machado, “Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste,” J. Clean. Prod., vol. 435, p. 140265, Jan. 2024, doi: 10.1016/j.jclepro.2023.140265.

P. Oguz-Ekim, “Machine Learning Approaches for Municipal Solid Waste Generation Forecasting,” Environ. Eng. Sci., vol. 38, no. 6, pp. 489–499, Jun. 2021, doi: 10.1089/ees.2020.0232.

G.-W. Cha, S.-H. Choi, W.-H. Hong, and C.-W. Park, “Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis,” Int. J. Environ. Res. Public Health, vol. 20, no. 4, p. 3159, Feb. 2023, doi: 10.3390/ijerph20043159.

M. H. Shahidzadeh, S. Shokouhyar, F. Javadi, and S. Shokoohyar, “Unscramble social media power for waste management: A multilayer deep learning approach,” J. Clean. Prod., vol. 377, p. 134350, Dec. 2022, doi: 10.1016/j.jclepro.2022.134350.

G.-W. Cha, W.-H. Hong, and Y.-C. Kim, “Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate,” Sustainability, vol. 15, no. 4, p. 3691, Feb. 2023, doi: 10.3390/su15043691.

G.-W. Cha, H. J. Moon, and Y.-C. Kim, “A hybrid machine-learning model for predicting the waste generation rate of building demolition projects,” J. Clean. Prod., vol. 375, p. 134096, Nov. 2022, doi: 10.1016/j.jclepro.2022.134096.

G.-W. Cha, S.-H. Choi, W.-H. Hong, and C.-W. Park, “Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas,” Int. J. Environ. Res. Public Health, vol. 20, no. 1, p. 107, Dec. 2022, doi: 10.3390/ijerph20010107.

P. Agrawal, G. Kaur, and S. S. Kolekar, “Investigation on biomedical waste management of hospitals using cohort intelligence algorithm,” Soft Comput. Lett., vol. 3, p. 100008, Dec. 2021, doi: 10.1016/j.socl.2020.100008.

S. Ahmad, Imran, F. Jamil, N. Iqbal, and D. Kim, “Optimal Route Recommendation for Waste Carrier Vehicles for Efficient Waste Collection: A Step Forward Towards Sustainable Cities,” IEEE Access, vol. 8, pp. 77875–77887, 2020, doi: 10.1109/ACCESS.2020.2988173.

M. Ghoreishi and A. Happonen, “Key enablers for deploying artificial intelligence for circular economy embracing sustainable product design: Three case studies,” in AIP Conference Proceedings, 2020, vol. 2233, p. 050008. doi: 10.1063/5.0001339.

T. Anh Khoa et al., “Waste Management System Using IoT-Based Machine Learning in University,” Wirel. Commun. Mob. Comput., vol. 2020, pp. 1–13, Feb. 2020, doi: 10.1155/2020/6138637.

M. Aazam, M. St-Hilaire, C.-H. Lung, and I. Lambadaris, “Cloud-based smart waste management for smart cities,” in 2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), Oct. 2016, pp. 188–193. doi: 10.1109/CAMAD.2016.7790356.

S. Selvakanmani, P. Rajeswari, B. V. Krishna, and J. Manikandan, “Optimizing E-waste management: Deep learning classifiers for effective planning,” J. Clean. Prod., vol. 443, p. 141021, Mar. 2024, doi: 10.1016/j.jclepro.2024.141021.

L. Andeobu, S. Wibowo, and S. Grandhi, “Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review,” Sci. Total Environ., vol. 834, p. 155389, Aug. 2022, doi: 10.1016/j.scitotenv.2022.155389.

Z. Fan, Z. Yan, and S. Wen, “Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health,” Sustainability, vol. 15, no. 18, p. 13493, Sep. 2023, doi: 10.3390/su151813493.

Mashudi, R. Sulistiowati, S. Handoyo, E. Mulyandari, and N. Hamzah, “Innovative Strategies and Technologies in Waste Management in the Modern Era Integration of Sustainable Principles, Resource Efficiency, and Environmental Impact,” Int. J. Sci. Soc., vol. 5, no. 4, pp. 87–100, Sep. 2023, doi: 10.54783/ijsoc.v5i4.767.

P. Nie, K. C. Dahanayake, and N. Sumanarathna, “Exploring UAE’s transition towards circular economy through construction and demolition waste management in the pre-construction stage–A case study approach,” Smart Sustain. Built Environ., vol. 13, no. 2, pp. 246–266, Feb. 2024, doi: 10.1108/SASBE-06-2022-0115.

K. H. Abdulkareem et al., “A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models,” Eng. Appl. Artif. Intell., vol. 132, p. 107926, Jun. 2024, doi: 10.1016/j.engappai.2024.107926.

C. J. Latha, K. Kalaiselvi, S. Ramanarayan, R. Srivel, S. Vani, and T. V. M. Sairam, “Dynamic convolutional neural network based e‐waste management and optimized collection planning,” Concurr. Comput. Pract. Exp., vol. 34, no. 17, Aug. 2022, doi: 10.1002/cpe.6941.

C. Lubongo and P. Alexandridis, “Assessment of Performance and Challenges in Use of Commercial Automated Sorting Technology for Plastic Waste,” Recycling, vol. 7, no. 2, p. 11, Feb. 2022, doi: 10.3390/recycling7020011.

R. Anitha, R. Maruthi, and S. Sudha, “Automated segregation and microbial degradation of plastic wastes: A greener solution to waste management problems,” Glob. Transitions Proc., vol. 3, no. 1, pp. 100–103, Jun. 2022, doi: 10.1016/j.gltp.2022.04.021.

P. Bründl, A. Scheck, H. G. Nguyen, and J. Franke, “Towards a circular economy for electrical products: A systematic literature review and research agenda for automated recycling,” Robot. Comput. Integr. Manuf., vol. 87, p. 102693, Jun. 2024, doi: 10.1016/j.rcim.2023.102693.

S. K. Baduge et al., “Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications,” Autom. Constr., vol. 141, p. 104440, Sep. 2022, doi: 10.1016/j.autcon.2022.104440.

S. Neelakandan et al., “Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management,” Chemosphere, vol. 308, p. 136046, Dec. 2022, doi: 10.1016/j.chemosphere.2022.136046.

J. Rajalakshmi, K. Sumangali, J. Jayanthi, and K. Muthulakshmi, “Artificial Intelligence with Earthworm Optimization Assisted Waste Management System for Smart Cities,” Glob. NEST J., vol. 25, no. 4, pp. 190–197, Jan. 2023, doi: 10.30955/gnj.004712.

V. Kakani, V. H. Nguyen, B. P. Kumar, H. Kim, and V. R. Pasupuleti, “A critical review on computer vision and artificial intelligence in food industry,” J. Agric. Food Res., vol. 2, p. 100033, Dec. 2020, doi: 10.1016/j.jafr.2020.100033.

W.-L. Mao, W.-C. Chen, C.-T. Wang, and Y.-H. Lin, “Recycling waste classification using optimized convolutional neural network,” Resour. Conserv. Recycl., vol. 164, p. 105132, Jan. 2021, doi: 10.1016/j.resconrec.2020.105132.

M. Koskinopoulou, F. Raptopoulos, G. Papadopoulos, N. Mavrakis, and M. Maniadakis, “Robotic Waste Sorting Technology: Toward a Vision-Based Categorization System for the Industrial Robotic Separation of Recyclable Waste,” IEEE Robot. Autom. Mag., vol. 28, no. 2, pp. 50–60, Jun. 2021, doi: 10.1109/MRA.2021.3066040.

A. K. Subramanian, D. Thayalan, A. I. Edwards, A. Almalki, and A. Venugopal, “Biomedical waste management in dental practice and its significant environmental impact: A perspective,” Environ. Technol. Innov., vol. 24, p. 101807, Nov. 2021, doi: 10.1016/j.eti.2021.101807.

X. Li, “SF EXPRESS Automated Robotic Sorting System Based on Machine Learning,” in Proceedings of the 2022 International Conference on Urban Planning and Regional Economy(UPRE 2022), 2022, vol. 654. doi: 10.2991/aebmr.k.220502.020.

A. Shreyas Madhav, R. Rajaraman, S. Harini, and C. C. Kiliroor, “Application of artificial intelligence to enhance collection of E-waste: A potential solution for household WEEE collection and segregation in India,” Waste Manag. Res. J. a Sustain. Circ. Econ., vol. 40, no. 7, pp. 1047–1053, Jul. 2022, doi: 10.1177/0734242X211052846.

Q. Li and C. Yu, “A Review of the Flexible Robotic Arm,” Appl. Comput. Eng., vol. 8, no. 1, pp. 274–279, Aug. 2023, doi: 10.54254/2755-2721/8/20230165.

R. Sarc, A. Curtis, L. Kandlbauer, K. Khodier, K. E. Lorber, and R. Pomberger, “Digitalisation and intelligent robotics in value chain of circular economy oriented waste management – A review,” Waste Manag., vol. 95, pp. 476–492, Jul. 2019, doi: 10.1016/j.wasman.2019.06.035.

T. Bag, “Socio-economic impacts of scientific-technological advancements,” Int. J. Multidiscip. Educ. Res., vol. 12, no. 8, pp. 70–99, 2023, [Online]. Available: https://s3-ap-southeast-1.amazonaws.com/ijmer/pdf/volume12/volume12-issue8(4)/13.pdf

R. Tiwari, “The Impact of AI and Machine Learning on Job Displacement and Employment Opportunities,” Int. J. Sci. Res. Eng. Manag., vol. 07, no. 01, Jan. 2023, doi: 10.55041/IJSREM17506.

A. Comninos, E. S. Muller, and G. Mutung’u, “Artificial Intelligence for Sustainable Human Development,” 2019. [Online]. Available: https://www.giswatch.org/node/6206

S. Ma, C. Zhou, C. Chi, Y. Liu, and G. Yang, “Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method,” Environ. Sci. Technol., vol. 54, no. 15, pp. 9609–9617, Aug. 2020, doi: 10.1021/acs.est.0c01802.

R. A. Ali, N. N. L. Nik Ibrahim, W. A. Wan Ab Karim Ghani, H. L. Lam, and N. S. Sani, “Utilization of process network synthesis and machine learning as decision-making tools for municipal solid waste management,” Int. J. Environ. Sci. Technol., vol. 19, no. 3, pp. 1985–1996, Mar. 2022, doi: 10.1007/s13762-021-03250-0.

B. Yan et al., “Fast identification and characterization of residual wastes via laser-induced breakdown spectroscopy and machine learning,” Resour. Conserv. Recycl., vol. 174, p. 105851, Nov. 2021, doi: 10.1016/j.resconrec.2021.105851.

J. Frankowski, M. Zaborowicz, J. Dach, W. Czekała, and J. Przybył, “Biological Waste Management in the Case of a Pandemic Emergency and Other Natural Disasters. Determination of Bioenergy Production from Floricultural Waste and Modeling of Methane Production Using Deep Neural Modeling Methods,” Energies, vol. 13, no. 11, p. 3014, Jun. 2020, doi: 10.3390/en13113014.

S. S. Mookkaiah, G. Thangavelu, R. Hebbar, N. Haldar, and H. Singh, “Design and development of smart Internet of Things–based solid waste management system using computer vision,” Environ. Sci. Pollut. Res., vol. 29, no. 43, pp. 64871–64885, Sep. 2022, doi: 10.1007/s11356-022-20428-2.

B. S. Costa et al., “Artificial Intelligence in Automated Sorting in Trash Recycling,” in Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018), Oct. 2018, pp. 198–205. doi: 10.5753/eniac.2018.4416.

E. Konstantinidis, A. Dakhel, C. Beretta, and A. Erlandsson, “Long-term effects of amyloid-beta deposits in human iPSC-derived astrocytes,” Mol. Cell. Neurosci., vol. 125, p. 103839, Jun. 2023, doi: 10.1016/j.mcn.2023.103839.

Y. Zhao and J. Li, “Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions,” Environ. Sci. Technol., vol. 56, no. 24, pp. 17531–17544, Dec. 2022, doi: 10.1021/acs.est.2c05874.

G. Maier, R. Gruna, T. Längle, and J. Beyerer, “A Survey of the State of the Art in Sensor-Based Sorting Technology and Research,” IEEE Access, vol. 12, pp. 6473–6493, 2024, doi: 10.1109/ACCESS.2024.3350987.

K. O. Wadatkar, “A Review on Internet of Things (IOT) Based Garbage Monitoring System,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. VII, pp. 3313–3317, Jul. 2021, doi: 10.22214/ijraset.2021.36844.

A. A. Kutty and G. M. Abdalla, “Tools and techniques for food security and sustainability related assessments: A focus on the data and food waste management system,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, 2020, no. August. [Online]. Available: https://index.ieomsociety.org/index.cfm/article/view/ID/4572

M. Anneken, M. Veerappa, M. F. Huber, C. Kühnert, F. Kronenwett, and G. Maier, “Explainable AI for sensor-based sorting systems,” Tech. Mess., vol. 90, no. 3, 2023, doi: 10.1515/teme-2022-0097.

C. Hoffmann Sampaio et al., “Construction and Demolition Waste Recycling through Conventional Jig, Air Jig, and Sensor-Based Sorting: A Comparison,” Minerals, vol. 11, no. 8, p. 904, Aug. 2021, doi: 10.3390/min11080904.

D. Peukert, C. Xu, and P. Dowd, “A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion,” Minerals, vol. 12, no. 11, p. 1364, Oct. 2022, doi: 10.3390/min12111364.

F. Zhang, Y. Lin, Y. Zhu, L. Li, X. Cui, and Y. Gao, “A Real-Time Sorting Robot System for Panax Notoginseng Taproots Equipped with an Improved Deeplabv3+ Model,” Agriculture, vol. 12, no. 8, p. 1271, Aug. 2022, doi: 10.3390/agriculture12081271.

M. Erkinay Ozdemir, Z. Ali, B. Subeshan, and E. Asmatulu, “Applying machine learning approach in recycling,” J. Mater. Cycles Waste Manag., vol. 23, no. 3, pp. 855–871, May 2021, doi: 10.1007/s10163-021-01182-y.

R. Chauhan, S. Shighra, H. Madkhali, L. Nguyen, and M. Prasad, “Efficient Future Waste Management: A Learning-Based Approach with Deep Neural Networks for Smart System (LADS),” Appl. Sci., vol. 13, no. 7, p. 4140, Mar. 2023, doi: 10.3390/app13074140.

C. S. Lee and D.-W. Lim, “CNN-Based Inspection Module for Liquid Carton Recycling by the Reverse Vending Machine,” Sustainability, vol. 14, no. 22, p. 14905, Nov. 2022, doi: 10.3390/su142214905.

J. Bobulski and M. Kubanek, “Waste Classification System Using Image Processing and Convolutional Neural Networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11507 LNCS, 2019, pp. 350–361. doi: 10.1007/978-3-030-20518-8_30.

D. Thanawala, A. Sarin, and P. Verma, “An Approach to Waste Segregation and Management Using Convolutional Neural Networks,” in Communications in Computer and Information Science, vol. 1244 CCIS, 2020, pp. 139–150. doi: 10.1007/978-981-15-6634-9_14.

M. Toğaçar, B. Ergen, and Z. Cömert, “Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models,” Measurement, vol. 153, p. 107459, Mar. 2020, doi: 10.1016/j.measurement.2019.107459.

M. Al Duhayyim et al., “Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management,” Sustainability, vol. 14, no. 18, p. 11704, Sep. 2022, doi: 10.3390/su141811704.

A. Chidepatil, P. Bindra, D. Kulkarni, M. Qazi, M. Kshirsagar, and K. Sankaran, “From Trash to Cash: How Blockchain and Multi-Sensor-Driven Artificial Intelligence Can Transform Circular Economy of Plastic Waste?,” Adm. Sci., vol. 10, no. 2, p. 23, Apr. 2020, doi: 10.3390/admsci10020023.

M. W. Rahman, R. Islam, A. Hasan, N. I. Bithi, M. M. Hasan, and M. M. Rahman, “Intelligent waste management system using deep learning with IoT,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 2072–2087, May 2022, doi: 10.1016/j.jksuci.2020.08.016.

A. Hosseinzadeh et al., “Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions,” Bioresour. Technol., vol. 303, no. 122926, p. 122926, May 2020, doi: 10.1016/j.biortech.2020.122926.

Imran, S. Ahmad, and D. H. Kim, “Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management,” IEEE Access, vol. 8, pp. 46193–46205, 2020, doi: 10.1109/ACCESS.2020.2979015.

V. T. Nguyen, Q. T. H. Ta, and P. K. T. Nguyen, “Artificial intelligence-based modeling and optimization of microbial electrolysis cell-assisted anaerobic digestion fed with alkaline-pretreated waste-activated sludge,” Biochem. Eng. J., vol. 187, p. 108670, Nov. 2022, doi: 10.1016/j.bej.2022.108670.

K. Bernat, “Post-Consumer Plastic Waste Management: From Collection and Sortation to Mechanical Recycling,” Energies, vol. 16, no. 8, p. 3504, Apr. 2023, doi: 10.3390/en16083504.

K. Pardini, J. J. P. C. Rodrigues, S. A. Kozlov, N. Kumar, and V. Furtado, “IoT-Based Solid Waste Management Solutions: A Survey,” J. Sens. Actuator Networks, vol. 8, no. 1, p. 5, Jan. 2019, doi: 10.3390/jsan8010005.

F. Alqahtani, Z. Al-Makhadmeh, A. Tolba, and W. Said, “Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm,” Cluster Comput., vol. 23, no. 3, pp. 1769–1780, Sep. 2020, doi: 10.1007/s10586-020-03126-x.

T. Bakhshi and M. Ahmed, “IoT-Enabled Smart City Waste Management using Machine Learning Analytics,” in 2018 2nd International Conference on Energy Conservation and Efficiency (ICECE), Oct. 2018, pp. 66–71. doi: 10.1109/ECE.2018.8554985.

G. Desogus, E. Quaquero, G. Rubiu, G. Gatto, and C. Perra, “BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings,” Sustainability, vol. 13, no. 8, p. 4496, Apr. 2021, doi: 10.3390/su13084496.

A. Namoun, A. Tufail, M. Y. Khan, A. Alrehaili, T. A. Syed, and O. BenRhouma, “Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges,” Sustainability, vol. 14, no. 20, p. 13578, Oct. 2022, doi: 10.3390/su142013578.

B. Fang et al., “Artificial intelligence for waste management in smart cities: a review,” Environ. Chem. Lett., vol. 21, no. 4, pp. 1959–1989, Aug. 2023, doi: 10.1007/s10311-023-01604-3.

A. Aljawder and W. Al-Karaghouli, “The adoption of technology management principles and artificial intelligence for a sustainable lean construction industry in the case of Bahrain,” J. Decis. Syst., vol. 33, no. 2, pp. 263–292, Apr. 2024, doi: 10.1080/12460125.2022.2075529.

T.-D. Bui and M.-L. Tseng, “Understanding the barriers to sustainable solid waste management in society 5.0 under uncertainties: a novelty of socials and technical perspectives on performance driving,” Environ. Sci. Pollut. Res., vol. 29, no. 11, pp. 16265–16293, Mar. 2022, doi: 10.1007/s11356-021-16962-0.

S. Vyas et al., “Solid waste management techniques powered by in-silico approaches with a special focus on municipal solid waste management: Research trends and challenges,” Sci. Total Environ., vol. 891, no. Issue3, p. 164344, Sep. 2023, doi: 10.1016/j.scitotenv.2023.164344.

L. M. Joshi, R. K. Bharti, and R. Singh, “Internet of Things and Machine Learning‐based Approaches in the Urban Solid Waste Management: Trends, Challenges, and Future Directions,” Expert Syst., vol. 39, no. 5, Jun. 2022, doi: 10.1111/exsy.12865.

A. Mounadel, H. Ech-Cheikh, S. Lissane Elhaq, A. Rachid, M. Sadik, and B. Abdellaoui, “Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review,” Environ. Technol. Rev., vol. 12, no. 1, pp. 316–336, Dec. 2023, doi: 10.1080/21622515.2023.2205027.

T.-H. Tsui, M. C. M. van Loosdrecht, Y. Dai, and Y. W. Tong, “Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams,” Bioresour. Technol., vol. 369, p. 128445, Feb. 2023, doi: 10.1016/j.biortech.2022.128445.

M. Z. Akkad, S. Haidar, and T. Bányai, “Design of Cyber-Physical Waste Management Systems Focusing on Energy Efficiency and Sustainability,” Designs, vol. 6, no. 2, 2022, doi: 10.3390/designs6020039.

G. Alam, I. Ihsanullah, M. Naushad, and M. Sillanpää, “Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects,” Chem. Eng. J., vol. 427, p. 130011, Jan. 2022, doi: 10.1016/j.cej.2021.130011.

J. Verma, “Deep Technologies Using Big Data in: Energy and Waste Management,” in Deep Learning Technologies for the Sustainable Development Goals, 2023, pp. 21–39. doi: 10.1007/978-981-19-5723-9_2.

Sanny Edinov and Rezki Fauzi, “Community Behavior in Artificial Intelligence-Based Waste Management,” Formosa J. Sustain. Res., vol. 2, no. 2, pp. 341–350, Feb. 2023, doi: 10.55927/fjsr.v2i2.2993.

J. Farjami, S. Dehyouri, and M. Mohamadi, “Evaluation of waste recycling of fruits based on Support Vector Machine (SVM),” Cogent Environ. Sci., vol. 6, no. 1, Jan. 2020, doi: 10.1080/23311843.2020.1712146.

G. U. Fayomi et al., “Smart Waste Management for Smart City: Impact on Industrialization,” IOP Conf. Ser. Earth Environ. Sci., vol. 655, no. 1, p. 012040, Feb. 2021, doi: 10.1088/1755-1315/655/1/012040.

S. O. Abioye et al., “Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges,” J. Build. Eng., vol. 44, p. 103299, Dec. 2021, doi: 10.1016/j.jobe.2021.103299.

A. Dimri, A. Nautiyal, and D. A. Vaish, “Outline Study and Development of Waste Bin and Wastage Recycling System in India,” Int. J. Tech. Res. Sci., vol. Special, no. Issue3, pp. 31–37, Oct. 2020, doi: 10.30780/specialissue-ICACCG2020/038.

N. Janbi, I. Katib, A. Albeshri, and R. Mehmood, “Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments,” Sensors, vol. 20, no. 20, p. 5796, Oct. 2020, doi: 10.3390/s20205796.

L. N. Thalluri et al., “Artificial Intelligence Enabled Smart City IoT System using Edge Computing,” in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), Oct. 2021, pp. 12–20. doi: 10.1109/ICOSEC51865.2021.9591732.

A. Aliyev, “The AI and Quantum Era: Transforming Project Management Practices,” J. Futur. Artif. Intell. Technol., vol. 1, no. 4, pp. 348–370, Jan. 2025, doi: 10.62411/faith.3048-3719-59.

S. Phuyal, D. Bista, and R. Bista, “Challenges, Opportunities and Future Directions of Smart Manufacturing: A State of Art Review,” Sustain. Futur., vol. 2, p. 100023, 2020, doi: 10.1016/j.sftr.2020.100023.

M. A. Al-Sharafi et al., “Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison,” Comput. Human Behav., vol. 143, p. 107708, Jun. 2023, doi: 10.1016/j.chb.2023.107708.

A. B. Brendel, M. Mirbabaie, T.-B. Lembcke, and L. Hofeditz, “Ethical Management of Artificial Intelligence,” Sustainability, vol. 13, no. 4, p. 1974, Feb. 2021, doi: 10.3390/su13041974.

T. Baddegama, H. Ariyasena, S. Wijethunga, M. Bowaththa, D. Nawinna, and B. Attanayake, “Solid-Waste Management System for Urban Sri Lanka Using IOT and Machine Learning,” in 2022 4th International Conference on Advancements in Computing (ICAC), Dec. 2022, pp. 222–227. doi: 10.1109/ICAC57685.2022.10025135.

G. K. Ijemaru, L.-M. Ang, and K. P. Seng, “Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management,” Sensors, vol. 23, no. 5, p. 2860, Mar. 2023, doi: 10.3390/s23052860.

S. Heikkilä, G. Malahat, and I. Deviatkin, “From waste to value: enhancing circular value creation in municipal solid waste management ecosystem through artificial intelligence-powered robots,” in Sustainable and Circular Management of Resources and Waste Towards a Green Deal, Elsevier, 2023, pp. 415–428. doi: 10.1016/B978-0-323-95278-1.00014-0.

S. Sharma and S. Shrestha, “Integrating HCI Principles in AI: A Review of Human-Centered Artificial Intelligence Applications and Challenges,” J. Futur. Artif. Intell. Technol., vol. 1, no. 3, pp. 309–317, Dec. 2024, doi: 10.62411/faith.3048-3719-47.

E. Singh, A. Kumar, R. Mishra, and S. Kumar, “Solid waste management during COVID-19 pandemic: Recovery techniques and responses,” Chemosphere, vol. 288, p. 132451, Feb. 2022, doi: 10.1016/j.chemosphere.2021.132451.

A. Martikkala, B. Mayanti, P. Helo, A. Lobov, and I. F. Ituarte, “Smart textile waste collection system – Dynamic route optimization with IoT,” J. Environ. Manage., vol. 335, p. 117548, Jun. 2023, doi: 10.1016/j.jenvman.2023.117548.

S. Mousavi, A. Hosseinzadeh, and A. Golzary, “Challenges, recent development, and opportunities of smart waste collection: A review,” Sci. Total Environ., vol. 886, no. 163925, p. 163925, Aug. 2023, doi: 10.1016/j.scitotenv.2023.163925.

A. E. Ba Alawi, A. Y. A. Saeed, F. Almashhor, R. Al-Shathely, and A. N. Hassan, “Solid Waste Classification Using Deep Learning Techniques,” in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Jul. 2021, pp. 1–5. doi: 10.1109/ICOTEN52080.2021.9493430.

J. Li and J. Huang, “The expansion of China’s solar energy: Challenges and policy options,” Renew. Sustain. Energy Rev., vol. 132, no. 110002, p. 110002, Oct. 2020, doi: 10.1016/j.rser.2020.110002.

M. T. Munir, B. Li, and M. Naqvi, “Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions,” Fuel, vol. 348, no. 128548, p. 128548, Sep. 2023, doi: 10.1016/j.fuel.2023.128548.

Y. Amirsoleymani, O. Abessi, and Y. E. Ghajari, “A spatial decision support system for municipal solid waste landfill sites (case study: The Mazandaran Province, Iran),” Waste Manag. Res. J. a Sustain. Circ. Econ., vol. 40, no. 7, pp. 940–952, Jul. 2022, doi: 10.1177/0734242X211060610.

J. Akinode and S. Oloruntoba, “Artificial Intelligience in the Transition to Circular Economy,” Am. J. Eng. Res., vol. 9, no. 6, pp. 185–190, 2020.

D. V. Yevle and P. S. Mann, “Artificial intelligence based classification for waste management: A survey based on taxonomy, classification & future direction,” Comput. Sci. Rev., vol. 56, p. 100723, May 2025, doi: 10.1016/j.cosrev.2024.100723.

Downloads

Published

2025-04-07

How to Cite

[1]
S. Fuqaha and N. Nursetiawan, “Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions”, J. Fut. Artif. Intell. Tech., vol. 2, no. 1, pp. 24–46, Apr. 2025.

Issue

Section

Articles