Design and evaluation of an intelligent waste monitoring system based on RGIS integration for smart cities

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.337033

Keywords:

monitoring system, waste management, geographic information system, routing, container, detection

Abstract

The object of this study is the municipal solid waste management system within a modern urban environment, where rapid urbanization and population growth pose significant challenges to ecological sustainability. The key problem addressed is the inefficiency of waste collection due to overflowing containers, poor route planning, and suboptimal resource allocation. To tackle these issues, an intelligent waste monitoring system has been developed that integrates Internet of Things (IoT) technologies, computer vision, data analytics, and a Regional Geographic Information System (RGIS). The system includes a computer vision model that analyzes images of waste containers to determine their fill level. Fine-tuning the model on locally collected image data, reflecting regional characteristics such as lighting, container types, and weather conditions, significantly improved detection accuracy and adaptability. Route optimization for waste collection is implemented using a mathematical formulation of the Traveling Salesman Problem (TSP), solved via Mixed Integer Linear Programming (MILP), which helped reduce fuel consumption, travel time, and staff workload. Integration with RGIS and GPS enables dynamic routing and real-time geospatial visualization of operational data. The proposed system forms a closed-loop control cycle that links automated detection, spatial analysis, and decision-making. Experimental results demonstrate high efficiency, adaptability to regional conditions, and scalability, confirming the system’s practical applicability to other urban areas. In the future, the system may be expanded to include environmental monitoring modules such as air quality, noise, and soil conditions and predictive modeling of waste generation, thereby supporting the sustainable development of smart city infrastructure

Author Biographies

Symbat Nurgaliyeva, Astana IT University

PhD in Computer Science

Department of Computer Engineering

Muratali Amangali, Astana IT University

Master’s Degree Student (2nd Year)

Department of Computer Engineering

Zhuldyz Basheyeva, Astana IT University

PhD

Department of Computer Engineering

Nurzhamal Kashkimbayeva, Astana IT University

PhD

Department of Computer Engineering

Daniyar Amantayev, Astana IT University

Master of Science

Department of Computer Engineering

References

  1. Kaza, S., Yao, L. C., Bhada-Tata, P., Van Woerden, F. (2018). What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-1329-0
  2. Thousands of tons of waste removed in Astana: How offenders are tracked (2025). Available at: https://tengrinews.kz/kazakhstan_news/astane-vyivozyat-tyisyachi-tonn-musora-lovyat-teh-ego-567746/
  3. Soni, G., Kandasamy, S. (2017). Smart Garbage Bin Systems – A Comprehensive Survey. Smart Secure Systems – IoT and Analytics Perspective, 194–206. https://doi.org/10.1007/978-981-10-7635-0_15
  4. Skol'ko v Kazakhstane potratili na sbor i pererabotku musora [How much was spent on waste collection and recycling in Kazakhstan] (2018). LSM.kz. Available at: https://lsm.kz/skol-ko-v-kazahstane-potratili-na-sbor-i-pererabotku-musora
  5. Aatamila, M., Verkasalo, P. K., Korhonen, M. J., Viluksela, M. K., Pasanen, K., Tiittanen, P., Nevalainen, A. (2010). Odor Annoyance near Waste Treatment Centers: A Population-Based Study in Finland. Journal of the Air &; Waste Management Association, 60 (4), 412–418. https://doi.org/10.3155/1047-3289.60.4.412
  6. Smailov, N., Tsyporenko, V., Sabibolda, A., Tsyporenko, V., Abdykadyrov, A., Kabdoldina, A. et al. (2024). Usprawnienie cyfrowego korelacyjno-interferometrycznego ustalania kierunku za pomocą przestrzennego sygnału analitycznego. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 14 (3), 43–48. https://doi.org/10.35784/iapgos.6177
  7. Agnew, C., Mewada, D., Grua, E. M., Eising, C., Denny, P., Heffernan, M. et al. (2023). Detecting the overfilled status of domestic and commercial bins using computer vision. Intelligent Systems with Applications, 18, 200229. https://doi.org/10.1016/j.iswa.2023.200229
  8. Department of Digital Technologies of Aktobe. Regional Geoinformation System of Aktobe. Available at: https://eaqtobe.kz/#/
  9. Roboflow. Garbage and trashes model (v4). Roboflow Universe. Available at: https://universe.roboflow.com/trash-and-garbage/garbage-and-trashes/model/4
  10. Ren, Y., Li, Y., Gao, X. (2024). An MRS-YOLO Model for High-Precision Waste Detection and Classification. Sensors, 24 (13), 4339. https://doi.org/10.3390/s24134339
  11. Lun, Z., Pan, Y., Wang, S., Abbas, Z., Islam, M. S., Yin, S. (2023). Skip-YOLO: Domestic Garbage Detection Using Deep Learning Method in Complex Multi-scenes. International Journal of Computational Intelligence Systems, 16 (1). https://doi.org/10.1007/s44196-023-00314-6
  12. Serik, M., Nurgaliyeva, S. (2024). Enhancing competence in mobile robot development: Integrating robotic technologies for future computer science teachers. Global Journal of Engineering Education, 26 (3), 205–211. Available at: https://www.wiete.com.au/journals/GJEE/Publish/vol26no3/11-Nurgaliyeva-S.pdf
  13. Smailov, N., Batyrgaliyev, A., Akhmediyarova, A., Seilova, N., Koshkinbayeva, M., Baigulbayeva, M. et al. (2020). Approaches to Evaluating the Quality of Masking Noise Interference. International Journal of Electronics and Telecommunications, 67 (1), 59–64. https://doi.org/10.24425/ijet.2021.135944
  14. Khan, S., Ali, B., Alharbi, A. A. K., Alotaibi, S., Alkhathami, M. (2024). Efficient IoT-Assisted Waste Collection for Urban Smart Cities. Sensors, 24 (10), 3167. https://doi.org/10.3390/s24103167
  15. Smailov, N., Zhadiger, T., Tashtay, Y., Abdykadyrov, A., Amir, A. (2024). Fiber laser-based two-wavelength sensors for detecting temperature and strain on concrete structures. International Journal of Innovative Research and Scientific Studies, 7 (4), 1693–1710. https://doi.org/10.53894/ijirss.v7i4.3481
  16. Momynkulov, Z., Dosbayev, Z., Suliman, A., Abduraimova, B., Smailov, N., Zhekambayeva, M., Zhamangarin, D. (2023). Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning. Computers, Materials & Continua, 75 (1), 2191–2208. https://doi.org/10.32604/cmc.2023.036205
  17. Kisała, P., Wójcik, W., Smailov, N., Kalizhanova, A., Harasim, D. (2015). Elongation determination using finite element and boundary element method. International Journal of Electronics and Telecommunications, 61 (4), 389–394. https://doi.org/10.2478/eletel-2015-0051
  18. Gupta, S. K., & Bhatia, R. K. (2017). Route Optimization of Municipal Solid Waste Collection in Jabalpur City using ARC GIS. International Journal of Trend in Scientific Research and Development, 2 (1), 457–464. https://doi.org/10.31142/ijtsrd7000
  19. Smailov, N., Orynbet, M., Nazarova, A., Torekhan, Z., Koshkinbayev, S., Yssyraiyl, K. et al. (2025). Optymalizacja pracy światłowodowych czujników w warunkach kosmicznych. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 15 (2), 130–134. https://doi.org/10.35784/iapgos.7200
  20. Kengesbayeva, S., Smailov, N., Tashtay, Y., Kiesewetter, D., Malyugin, V., Amir, A. (2024). Research of Deformation of Concrete Structures Using Fiber Optic Sensors and Bragg Gratings. 2024 International Conference on Electrical Engineering and Photonics (EExPolytech), 15–18. https://doi.org/10.1109/eexpolytech62224.2024.10755828
  21. Sekenov, B., Smailov, N., Tashtay, Y., Amir, A., Kuttybayeva, A., Tolemanova, A. (2024). Fiber-Optic Temperature Sensors for Monitoring the Influence of the Space Environment on Nanosatellites: A Review. Advances in Asian Mechanism and Machine Science, 371–380. https://doi.org/10.1007/978-3-031-67569-0_42
  22. Smailov, N., Akmardin, S., Ayapbergenova, A., Ayapbergenova, G., Kadyrova, R., Sabibolda, A. (2025). Analiza wydajności VLC w optycznych systemach komunikacji bezprzewodowej do zastosowań wewnętrznych. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 15 (2), 135–138. https://doi.org/10.35784/iapgos.6971
  23. Wójcik, W., Kalizhanova, A., Kulyk, Y., Knysh, B., Kvyetnyy, R., Kulyk, A. et al. (2022). The Method of Time Distribution for Environment Monitoring Using Unmanned Aerial Vehicles According to an Inverse Priority. Journal of Ecological Engineering, 23 (11), 179–187. https://doi.org/10.12911/22998993/153458
  24. Sabibolda, A., Tsyporenko, V., Tsyporenko, V., Smailov, N., Zhunussov, K., Abdykadyrov, A. et al. (2022). Improving the accuracy and performance speed of the digital spectral-correlation method for measuring delay in radio signals and direction finding. Eastern-European Journal of Enterprise Technologies, 1 (9 (115)), 6–14. https://doi.org/10.15587/1729-4061.2022.252561
  25. Mikhailov, P., Ualiyev, Z., Kabdoldina, A., Smailov, N., Khikmetov, A., Malikova, F. (2021). Multifunctional fiber-optic sensors for space infrastructure. Eastern-European Journal of Enterprise Technologies, 5 (5 (113)), 80–89. https://doi.org/10.15587/1729-4061.2021.242995
  26. Smailov, N., Uralova, F., Kadyrova, R., Magazov, R., Sabibolda, A. (2025). Optymalizacja metod uczenia maszynowego do deanonimizacji w sieciach społecznościowych. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 15 (1), 101–104. https://doi.org/10.35784/iapgos.7098
Design and evaluation of an intelligent waste monitoring system based on RGIS integration for smart cities

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Published

2025-08-29

How to Cite

Nurgaliyeva, S., Amangali, M., Basheyeva, Z., Kashkimbayeva, N., & Amantayev, D. (2025). Design and evaluation of an intelligent waste monitoring system based on RGIS integration for smart cities. Eastern-European Journal of Enterprise Technologies, 4(9 (136), 70–78. https://doi.org/10.15587/1729-4061.2025.337033

Issue

Vol. 4 No. 9 (136) (2025): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2025 Symbat Nurgaliyeva, Muratali Amangali, Zhuldyz Basheyeva, Nurzhamal Kashkimbayeva, Daniyar Amantayev

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