A GeoAI framework for detecting risk zones from illegal dumping sites in Khulna, Bangladesh
Swadhin Das, Md. Manjur Morshed
TL;DR
This study uses GeoAI to detect illegal dumping sites in Khulna, Bangladesh, helping improve waste management and urban planning.
Contribution
The study introduces the first GeoAI framework for illegal dumping site detection in Khulna, Bangladesh.
Findings
Random Forest achieved the highest AUC (0.883) and F1 score (0.820) in detecting illegal dumping sites.
Proximity to roads, drains, and buildings were the most influential predictors of illegal dumping.
High-risk zones are concentrated along roads and urban centers in Khulna.
Abstract
Illegal dumping is a major challenge of municipal solid waste management. A significant portion of waste is dumped illegally in cities worldwide. This improper waste disposal creates serious ecological degradation, environmental hazards, public health risks, and urban planning challenges. This study presents a Positive-Unlabeled (PU) learning framework for detecting illegal dumping sites using GeoAI. Seven machine learning algorithms were trained on 70% of 341 confirmed illegal dumping sites across 10 m × 10 m resolution grids covering Khulna City Corporation, Bangladesh. The remaining 30% of the data was reserved for independent testing. The best model was selected using a weighted composite score combining the Area Under the Curve (AUC) and F1 score from spatial cross-validation, and the selected best model was subsequently evaluated on an independent hold-out test set. Random Forest…
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Taxonomy
TopicsMunicipal Solid Waste Management · Landfill Environmental Impact Studies · Microplastics and Plastic Pollution