Enhancing municipal solid waste management efficiency through clustering: a case study
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Date
2024-11
Authors
Çil, Sedat
Karaer, Feza
Salihoğlu, N. Kamil
Tabansız Göç, Gülveren
Çavdur, Fatih
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor and Francis
Abstract
This study leverages real-time datasets generated through IoT technology and smart city applications to enhance solid waste management in Yalova Province, Turkey. By integrating these datasets with the municipality’s Geographic Information System (GIS) using the ITRF/96 3 UTM X Y Coordinate System, a dynamic waste collection framework was established. The K-Means clustering algorithm was employed to determine the optimal waste container placement, considering capacities of 550, 800, 1,000, and 3,000 liters and walking distances of 50–100 ms. Results indicated that 1,000 and 3,000-liter containers with a 100-m walking distance maximized collection efficiency. Replacing 484 traditional containers with 105 units of 3,000 liters reduced total routes by 34%, transport costs by 42.2%, and CO2 emissions by 33.5%. The study underscores the importance of integrating GIS and IoT technologies for real-time waste management, aligning with the UN’s Sustainable Development Goals (SDG 11 and SDG 13). By combining
data-driven decision-making with urban sustainability practices, it offers a replicable model for municipalities seeking to reduce costs and environmental impacts in waste collection.
Description
Science Citation Index Expanded (SCI-EXPANDED)
Keywords
Algorithm , clustering , municipal solid waste management , optimization , smart city , sustainability
Citation
Çіl, S., Karaer, F., Salihoglu, N. K., Tabansiz-Goc, G., & Cavdur, F. (2024). Enhancing municipal solid waste management efficiency through clustering: a case study. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 46(1), 17304–17314. https://doi.org/10.1080/15567036.2024.2435540
