Towards Sustainable Waste Management: Predictive Modelling of Illegal Dumping Risk Zones Using Circular Data Loops and Remote Sensing

Borut Hojnik, Gregor Horvat, Domen Mongus, Matej Brumen and Rok Kamnik ()
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Borut Hojnik: Javno Podjetje Nigrad, Komunalno Podjetje d.o.o., 2000 Maribor, Slovenia
Gregor Horvat: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia
Domen Mongus: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia
Matej Brumen: Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia
Rok Kamnik: Department of Operational Construction, Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

Sustainability, 2025, vol. 17, issue 18, 1-33

Abstract: Illegal waste dumping poses a severe challenge to sustainable urban and regional development, undermining environmental integrity, public health, and the efficient use of resources. This study contributes to sustainability science by proposing a circular data feedback loop that enables dynamic, scalable, and cost-efficient monitoring and prevention of illegal dumping, aligned with the goals of sustainable waste governance. Historical data from the Slovenian illegal dumping register, UAV-based surveys and a newly developed application were used to update, monitor, and validate waste site locations. A comprehensive risk model, developed using machine learning methods, was created for the Municipality of Maribor (Slovenia). The modelling approach combined unsupervised and semi-supervised learning techniques, suitable for a positive-unlabeled (PU) dataset structure, where only confirmed illegal waste dumping sites were labeled. The approach demonstrates the feasibility of a circular data feedback loop integrating updated field data and predictive analytics to support waste management authorities and illegal waste dumping prevention. The fundamental characteristic of the stated approach is that each iteration of the loop improves the prediction of risk areas, providing a high-quality database for conducting targeted UAV overflights and consequently detecting locations of illegally dumped waste (LNOP) risk areas. At the same time, information on risk areas serves as the primary basis for each field detection of new LNOPs. The proposed model outperforms earlier approaches by addressing smaller and less conspicuous dumping events and by enabling systematic, technology-supported detection and prevention planning.

Keywords: circular data loop; illegal dumping; illegal waste location prediction model; GIS analysis; remote sensing; machine learning; unsupervised learning; semi-supervised learning; UAV illegal waste data collection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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