Application of artificial intelligence for optimizing solid waste management in rural municipalities: a systematic review

Authors

DOI:

https://doi.org/10.51252/rcsi.v5i2.960

Keywords:

artificial intelligence, automation, municipalities, optimization, waste

Abstract

This systematic review examines the application of artificial intelligence (AI) technologies in solid waste management in rural municipalities, aiming to identify the advances, benefits, challenges, and research gaps in this emerging field. Based on the analysis of 47 scientific articles selected from the Scopus database, various technological solutions were identified, including computer vision systems, deep learning, predictive models, IoT sensors, and hybrid Edge-Cloud platforms. The results show substantial improvements in waste classification, operational planning, and energy efficiency, although significant limitations associated with technological infrastructure, data availability, and technical training in rural areas persist. The review also highlights research opportunities aimed at developing lightweight models, integrating local knowledge, and generating open and representative datasets. These findings allow us to propose strategic lines for the sustainable adoption of AI in rural environments, contributing to the digital transformation of public services with a territorial focus.

References

Ahmad, K., Khan, K., & Al-Fuqaha, A. (2020). Intelligent Fusion of Deep Features for Improved Waste Classification. IEEE Access, 8, 96495-96504. https://doi.org/10.1109/ACCESS.2020.2995681 DOI: https://doi.org/10.1109/ACCESS.2020.2995681

Ahmed, K., Kumar Dubey, M., Kumar, A., & Dubey, S. (2024). Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review. Measurement: Sensors, 36. https://doi.org/10.1016/j.measen.2024.101395 DOI: https://doi.org/10.1016/j.measen.2024.101395

Alsabt, R., Alkhaldi, W., Adenle, Y. A., & Alshuwaikhat, H. M. (2024). Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study. Cleaner Waste Systems, 8, 100158. https://doi.org/10.1016/j.clwas.2024.100158 DOI: https://doi.org/10.1016/j.clwas.2024.100158

Alzoubi, Y. I., & Mishra, A. (2024). Green artificial intelligence initiatives: Potentials and challenges. Journal of Cleaner Production, 468, 143090. https://doi.org/10.1016/j.jclepro.2024.143090 DOI: https://doi.org/10.1016/j.jclepro.2024.143090

Amante, B., Puig, A., Zamora, J. L., Moreno, J., & Sanfeliu, A. (2024). Robots in waste management. Detritus, 29, 179-190. https://doi.org/10.31025/2611-4135/2024.19430 DOI: https://doi.org/10.31025/2611-4135/2024.19430

Anaya Figueroa, T., Montalvo Castro, J., Calderón, A. I., & Arispe Alburqueque, C. (2021). Escuelas rurales en el Perú: factores que acentúan las brechas digitales en tiempos de pandemia (COVID- 19) y recomendaciones para reducirlas. Educación, 30(58). https://doi.org/10.18800/educacion.202101.001 DOI: https://doi.org/10.18800/educacion.202101.001

Bchir, O., Alghannam, S., Alsadhan, N., Alsumairy, R., Albelahid, R., & Almotlaq, M. (2021). Computer Vision based Polyethylene Terephthalate (PET) Sorting for Waste Recycling. International Journal of Advanced Computer Science and Applications, 12(10), 624-633. https://doi.org/10.14569/IJACSA.2021.0121069 DOI: https://doi.org/10.14569/IJACSA.2021.0121069

Belsare, K., Singh, M., Gandam, A., Samudrala, V., Singh, R., F. Soliman, N., Das, S., & D. Algarni, A. (2024). Wireless sensor network-based machine learning framework for smart cities in intelligent waste management. Heliyon, 10(16). https://doi.org/10.1016/j.heliyon.2024.e36271 DOI: https://doi.org/10.1016/j.heliyon.2024.e36271

Boostani, F., Golzary, A., Huisingh, D., & Skitmore, M. (2024). Mapping the future: Unveiling global trends in smart waste management research. Results in Engineering, 24. https://doi.org/10.1016/j.rineng.2024.103485 DOI: https://doi.org/10.1016/j.rineng.2024.103485

Boresta, M., Croella, A. L., Gentile, C., Palagi, L., Pinto, D. M., Stecca, G., & Ventura, P. (2024). Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome. Logistics, 8(3). https://doi.org/10.3390/logistics8030079 DOI: https://doi.org/10.3390/logistics8030079

Carranza, L. (2013). Nuevas tecnologías, gobierno local y participación ciudadana: el caso de la Municipalidad de San Borja. Canalé, 5, 83-90. https://revistas.pucp.edu.pe/index.php/canale/article/view/14711

Chen, H. (2021). Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology. Advances in Mathematical Physics, 2021. https://doi.org/10.1155/2021/4094684 DOI: https://doi.org/10.1155/2021/4094684

Chhabra, M., Sharan, B., Elbarachi, M., & Kumar, M. (2024). Intelligent waste classification approach based on improved multi-layered convolutional neural network. Multimedia Tools and Applications, 83(36), 84095-84120. https://doi.org/10.1007/s11042-024-18939-w DOI: https://doi.org/10.1007/s11042-024-18939-w

Chowdhury, S. S., Hossain, N. B., Saha, T., Ferdous, J., & Zishan, M. S. R. (2021). Design and implementation of an autonomous waste sorting machine using machine learning technique. AIUB Journal of Science and Engineering, 19(3), 134-142. https://doi.org/10.53799/AJSE.V19I3.104 DOI: https://doi.org/10.53799/ajse.v19i3.104

Endah, S. N., Kusumaningrum, R., Sasongko, P. S., & Nisa, I. Z. (2021). Solid waste classification using pyramid scene parsing network segmentation and combined features. Telkomnika (Telecommunication Computing Electronics and Control), 19(6), 1902-1912. https://doi.org/10.12928/TELKOMNIKA.v19i6.18402 DOI: https://doi.org/10.12928/telkomnika.v19i6.18402

España-Merchán, A. Y. (2023). Responsabilidad Social Empresarial hacia la implementación de prácticas ambientales en Ecuador. Revista Amazónica de Ciencias Económicas, 2(2), e475. https://doi.org/10.51252/race.v2i2.475 DOI: https://doi.org/10.51252/race.v2i2.475

Fernández-Sánchez, H., King, K., & Enríquez-Hernández, C. B. (2020). Revisiones Sistemáticas Exploratorias como metodología para la síntesis del conocimiento científico. Enfermería Universitaria, 17(1). https://doi.org/10.22201/eneo.23958421e.2020.1.697 DOI: https://doi.org/10.22201/eneo.23958421e.2020.1.697

Fernandez, A., Zalazar-García, D., Lorenzo-Doncel, C., Yepes Maya, D. M., Silva Lora, E. E., Rodriguez, R., & Mazza, G. (2024). Kinetic Modeling of Co-Pyrogasification in Municipal Solid Waste (MSW) Management: Towards Sustainable Resource Recovery and Energy Generation. Sustainability (Switzerland), 16(10). https://doi.org/10.3390/su16104056 DOI: https://doi.org/10.3390/su16104056

Herrera-Granda, I. D., Cadena-Echeverría, J., León-Jácome, J. C., Herrera-Granda, E. P., Chavez Garcia, D., & Rosales, A. (2024). A Heuristic Procedure for Improving the Routing of Urban Waste Collection Vehicles Using ArcGIS. Sustainability (Switzerland), 16(13). https://doi.org/10.3390/su16135660 DOI: https://doi.org/10.3390/su16135660

Karademir, M., & Özbakır Acımert, B. A. (2024). Sustainable Waste Governance Framework via Web-GIS: Kadikoy Case. Sustainability (Switzerland), 16(16). https://doi.org/10.3390/su16167171 DOI: https://doi.org/10.3390/su16167171

Khan, R., Kumar, S., Srivastava, A. K., Dhingra, N., Gupta, M., Bhati, N., & Kumari, P. (2021). Machine Learning and IoT-Based Waste Management Model. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/5942574 DOI: https://doi.org/10.1155/2021/5942574

Koinig, G., Kuhn, N., Fink, T., Lorber, B., Radmann, Y., Martinelli, W., & Tischberger-Aldrian, A. (2024). Deep learning approaches for classification of copper-containing metal scrap in recycling processes. Waste Management, 190, 520-530. https://doi.org/10.1016/j.wasman.2024.10.022 DOI: https://doi.org/10.1016/j.wasman.2024.10.022

Labambe, M. R., Ardiansyah, R., & Pratama, S. A. (2024). Predicting Waste Production Trends in Palu City Using Linear Regression Analysis. Advance Sustainable Science, Engineering and Technology, 6(3). https://doi.org/10.26877/asset.v6i3.523 DOI: https://doi.org/10.26877/asset.v6i3.523

Laureti, L., Costantiello, A., Anobile, F., Leogrande, A., & Magazzino, C. (2024). Waste Management and Innovation: Insights from Europe. Recycling, 9(5). https://doi.org/10.3390/recycling9050082 DOI: https://doi.org/10.3390/recycling9050082

Li, P., Xu, J., & Liu, S. (2024). Solid Waste Detection Using Enhanced YOLOv8 Lightweight Convolutional Neural Networks. Mathematics, 12(14). https://doi.org/10.3390/math12142185 DOI: https://doi.org/10.3390/math12142185

López-Noguero, F., Morón-Marchena, J. A., García-Lázaro, I., & Gallardo-López, J. A. (2024). El formato de la participación ciudadana desde la digitalización municipal. Revista de Ciencias Sociales, 3(46). https://revistaprismasocial.es/article/view/5481/6006

Lubongo, C., Bin Daej, M. A. A., & Alexandridis, P. (2024). Recent Developments in Technology for Sorting Plastic for Recycling: The Emergence of Artificial Intelligence and the Rise of the Robots. Recycling, 9(4). https://doi.org/10.3390/recycling9040059 DOI: https://doi.org/10.3390/recycling9040059

Madueño Cairo, A., & Roca Becerra, J. L. (2025). Impacto de la IA en la optimización de la calidad de servicio en la gestión de residuos sólidos en una empresa: Revisión sistemática de literatura. Ciencias e Ingeniería, 1(1). https://ctscafe.pe/index.php/cienciaingenieria/article/view/366

Manakkakudy Kumaran, A., De Iacovo, A., Ballabio, A., Frigerio, J., Isella, G., & Colace, L. (2024). Waste Material Classification Based on a Wavelength-Sensitive Ge-on-Si Photodetector. Sensors, 24(21). https://doi.org/10.3390/s24216970 DOI: https://doi.org/10.3390/s24216970

Manchado Garabito, R., Tamames Gómez, S., López González, M., Mohedano Macías, L., D´Agostino, M., & Veiga de Cabo, J. (2009). Revisiones Sistemáticas Exploratorias. Medicina y Seguridad del Trabajo, 55(216), 12-19. https://doi.org/10.4321/s0465-546x2009000300002 DOI: https://doi.org/10.4321/S0465-546X2009000300002

Melinte, D. O., Travediu, A.-M., & Dumitriu, D. N. (2020). Deep convolutional neural networks object detector for real-time waste identification. Applied Sciences (Switzerland), 10(20), 1-18. https://doi.org/10.3390/app10207301 DOI: https://doi.org/10.3390/app10207301

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 339(jul21 1), b2535-b2535. https://doi.org/10.1136/bmj.b2535 DOI: https://doi.org/10.1136/bmj.b2535

Nedjar, I., M’hamedi, M., & Bekkaoui, M. (2024). Real-Time Solid Waste Sorting Machine Based on Deep Learning. International Journal of Electrical and Computer Engineering Systems, 15(7), 581-589. https://doi.org/10.32985/ijeces.15.7.4 DOI: https://doi.org/10.32985/ijeces.15.7.4

Olawumi, M. A., Oladapo, B. I., & Olawale, R. A. (2024). Revolutionising waste management with the impact of Long Short-Term Memory networks on recycling rate predictions. Waste Management Bulletin, 2(3), 266-274. https://doi.org/10.1016/j.wmb.2024.08.006 DOI: https://doi.org/10.1016/j.wmb.2024.08.006

Olivieri, F., Caputo, A., Leonetti, D., Castaldo, R., Avolio, R., Cocca, M., Errico, M. E., Iannotta, L., Avella, M., Carfagna, C., & Gentile, G. (2024). Compositional Analysis and Mechanical Recycling of Polymer Fractions Recovered via the Industrial Sorting of Post-Consumer Plastic Waste: A Case Study toward the Implementation of Artificial Intelligence Databases. Polymers, 16(20). https://doi.org/10.3390/polym16202898 DOI: https://doi.org/10.3390/polym16202898

Onoda, H. (2020). Smart approaches to waste management for post-COVID-19 smart cities in Japan. IET Smart Cities, 2(2), 89-94. https://doi.org/10.1049/iet-smc.2020.0051 DOI: https://doi.org/10.1049/iet-smc.2020.0051

Palmieri, R., Gasbarrone, R., Bonifazi, G., Piccinini, G., & Serranti, S. (2024). Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling. Applied Sciences (Switzerland), 14(23). https://doi.org/10.3390/app142311437 DOI: https://doi.org/10.3390/app142311437

Pandey, D. S., Pan, I., Das, S., Leahy, J. J., & Kwapinski, W. (2015). Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. Bioresource Technology, 179, 524-533. https://doi.org/10.1016/j.biortech.2014.12.048 DOI: https://doi.org/10.1016/j.biortech.2014.12.048

Pitakaso, R., Srichok, T., Khonjun, S., Golinska-Dawson, P., Sethanan, K., Nanthasamroeng, N., Gonwirat, S., Luesak, P., & Boonmee, C. (2024). Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management. Engineering Applications of Artificial Intelligence, 133. https://doi.org/10.1016/j.engappai.2024.108614 DOI: https://doi.org/10.1016/j.engappai.2024.108614

Pulparambil, S., Al-Busaidi, A., Al-Hatimy, Y., & Al-Farsi, A. (2024). Internet of things-based smart medical waste management system. Telematics and Informatics Reports, 15. https://doi.org/10.1016/j.teler.2024.100161 DOI: https://doi.org/10.1016/j.teler.2024.100161

Quintana Ruidías, H. D. (2025). Transformación digital en la administración pública y la gestión de gobierno de una municipalidad distrital en Piura. Revista InveCom, 5(2), 1-10. https://revistainvecom.org/index.php/invecom/article/view/3390

Raza-Carrillo, D., & Acosta, J. (2022). Planificación ambiental y el reciclaje de desechos sólidos urbanos. Economía Sociedad y Territorio, 22(69), 519-544. https://doi.org/10.22136/est20221696 DOI: https://doi.org/10.22136/est20221696

Rekabi, S., Sazvar, Z., & Goodarzian, F. (2024). A bi-objective sustainable vehicle routing optimization model for solid waste networks with internet of things. Supply Chain Analytics, 5. https://doi.org/10.1016/j.sca.2024.100059 DOI: https://doi.org/10.1016/j.sca.2024.100059

Saeed, M., Ahsan, M., Saeed, M. H., Mehmood, A., & El-Morsy, S. (2021). Assessment of Solid Waste Management Strategies Using an Efficient Complex Fuzzy Hypersoft Set Algorithm Based on Entropy and Similarity Measures. IEEE Access, 9, 150700-150714. https://doi.org/10.1109/ACCESS.2021.3125727 DOI: https://doi.org/10.1109/ACCESS.2021.3125727

Sallang, N. C. A., Islam, M. T., Islam, M. S., & Arshad, H. (2021). A CNN-Based Smart Waste Management System Using TensorFlow Lite and LoRa-GPS Shield in Internet of Things Environment. IEEE Access, 9, 153560-153574. https://doi.org/10.1109/ACCESS.2021.3128314 DOI: https://doi.org/10.1109/ACCESS.2021.3128314

Sanchez-Yañez, J. M., & Marquez-Benavides, L. (2023). Gestión de residuos sólidos y la inteligencia artificial en el contexto mexicano. Ciencia Nicolaita, 91(11), 196-207. DOI: https://doi.org/10.35830/cn.vi90.722

Sharma, M., Joshi, S., Kannan, D., Govindan, K., Singh, R., & Purohit, H. C. (2020). Internet of Things (IoT) adoption barriers of smart cities’ waste management: An Indian context. Journal of Cleaner Production, 270. https://doi.org/10.1016/j.jclepro.2020.122047 DOI: https://doi.org/10.1016/j.jclepro.2020.122047

Sharma, R. K., & Jailia, M. (2024). Garbage prediction using regression analysis for municipal corporations of Indian cities. Cognitive Computation and Systems, 6(4), 74-85. https://doi.org/10.1049/ccs2.12103 DOI: https://doi.org/10.1049/ccs2.12103

Shukhratov, I., Pimenov, A., Stepanov, A., Mikhailova, N., Baldycheva, A., & Somov, A. (2024). Optical detection of plastic waste through computer vision. Intelligent Systems with Applications, 22. https://doi.org/10.1016/j.iswa.2024.200341 DOI: https://doi.org/10.1016/j.iswa.2024.200341

Sirimewan, D., Kunananthaseelan, N., Raman, S., Garcia, R., & Arashpour, M. (2024). Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision. Waste Management, 190, 149-160. https://doi.org/10.1016/j.wasman.2024.09.018 DOI: https://doi.org/10.1016/j.wasman.2024.09.018

Straka, M., Khouri, S., Rosova, A., Caganova, D., & Culkova, K. (2018). Utilization of computer simulation for waste separation design as a logistics system. International Journal of Simulation Modelling, 17(4), 583-596. https://doi.org/10.2507/IJSIMM17(4)444 DOI: https://doi.org/10.2507/IJSIMM17(4)444

Tryhuba, I., Tryhuba, A., Hutsol, T., Cieszewska, A., Andrushkiv, O., Glowacki, S., Bryś, A., Slobodian, S., Tulej, W., & Sojak, M. (2024). Prediction of Biogas Production Volumes from Household Organic Waste Based on Machine Learning. Energies, 17(7). https://doi.org/10.3390/en17071786 DOI: https://doi.org/10.3390/en17071786

Ulloa-Gallardo, N. J., Isuiza-Pérez, D. D., Contreras-Aguilar, J. A., & Caviedes-Contreras, W. (2022). Dispositivo para el monitoreo de calidad de agua en el consumo humano en Puerto Maldonado – Cusco. Revista Amazonía Digital, 1(1), e162. https://doi.org/10.55873/rad.v1i1.162 DOI: https://doi.org/10.55873/rad.v1i1.162

Vallejo, P., Correa, D., Arbeláez, J. C., Tabares, M. S., Ruiz-Arenas, S., Rendon-Velez, E., Ríos-Zapata, D., & Alvarado, J. (2024). EcoMind: Web-based waste labeling tool. SoftwareX, 26. https://doi.org/10.1016/j.softx.2024.101684 DOI: https://doi.org/10.1016/j.softx.2024.101684

Vesga Ferreira, J. C., Sepulveda, F. A. A., & Perez Waltero, H. E. (2024). Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia. Sustainability (Switzerland), 16(13). https://doi.org/10.3390/su16135300 DOI: https://doi.org/10.3390/su16135300

Wang, H., Qian, L.-P., Xu, L.-Y., Li, Y., & Guan, H. (2024). Upcycling of waste rubber using pelletized artificial geopolymer aggregate technology. Developments in the Built Environment, 20. https://doi.org/10.1016/j.dibe.2024.100554 DOI: https://doi.org/10.1016/j.dibe.2024.100554

Wang, Z., Yang, X., Zheng, X., Huang, D., & Jiang, B. (2024). Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing. Buildings, 14(12). https://doi.org/10.3390/buildings14123999 DOI: https://doi.org/10.3390/buildings14123999

Yazdani, M., Kabirifar, K., & Haghani, M. (2024). Optimising post-disaster waste collection by a deep learning-enhanced differential evolution approach. Engineering Applications of Artificial Intelligence, 132. https://doi.org/10.1016/j.engappai.2024.107932 DOI: https://doi.org/10.1016/j.engappai.2024.107932

Zaeimi, M. B., & Rassafi, A. A. (2021). Optimization Model for Integrated Municipal Solid Waste System Using Stochastic Chance-Constraint Programming under Uncertainty: A Case Study in Qazvin, Iran. Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/9994853 DOI: https://doi.org/10.1155/2021/9994853

Zhao, H., Wang, Y., Liu, X., Wang, X., Chen, Z., Lei, Z., Zhou, Y., & Singh, A. (2024). Corrigendum to “Review on solid wastes incorporated cementitious material using 3D concrete printing technology”. Case Studies in Construction Materials, 21. https://doi.org/10.1016/j.cscm.2024.e03922 DOI: https://doi.org/10.1016/j.cscm.2024.e03676

Zheng, H., & Gu, Y. (2021). Encnn-upmws: Waste classification by a CNN ensemble using the UPM weighting strategy. Electronics (Switzerland), 10(4), 1-21. https://doi.org/10.3390/electronics10040427 DOI: https://doi.org/10.3390/electronics10040427

Zulhusni, M., Sari, C. A., & Rachmawanto, E. H. (2024). Implementation of DenseNet121 Architecture for Waste Type Classification. Advance Sustainable Science, Engineering and Technology, 6(3). https://doi.org/10.26877/asset.v6i3.673 DOI: https://doi.org/10.26877/asset.v6i3.673

Downloads

Published

2025-07-20

How to Cite

Arista-López, D. R. (2025). Application of artificial intelligence for optimizing solid waste management in rural municipalities: a systematic review. Revista Científica De Sistemas E Informática, 5(2), e960. https://doi.org/10.51252/rcsi.v5i2.960