AI-Enabled Waste Classification as a Data-Driven Decision Support Tool for Circular Economy and Urban Sustainability

• URL: http://arxiv.org/abs/2601.22418v1
• Date: Fri, 30 Jan 2026 00:10:40 GMT
• Title: AI-Enabled Waste Classification as a Data-Driven Decision Support Tool for Circular Economy and Urban Sustainability
• Authors: Julius Sechang Mboli, Omolara Aderonke Ogungbemi,
• Abstract summary: This paper evaluates both traditional machine-learning (Random Forest, SVM, AdaBoost) and deep-learning techniques.<n>We show how these models integrate into a real-time Data-Driven Decision Support System for automated waste sorting.
• Score: 0.3867363075280543
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Efficient waste sorting is crucial for enabling circular-economy practices and resource recovery in smart cities. This paper evaluates both traditional machine-learning (Random Forest, SVM, AdaBoost) and deep-learning techniques including custom CNNs, VGG16, ResNet50, and three transfer-learning models (DenseNet121, EfficientNetB0, InceptionV3) for binary classification of 25 077 waste images (80/20 train/test split, augmented and resized to 150x150 px). The paper assesses the impact of Principal Component Analysis for dimensionality reduction on traditional models. DenseNet121 achieved the highest accuracy (91 %) and ROC-AUC (0.98), outperforming the best traditional classifier by 20 pp. Principal Component Analysis (PCA) showed negligible benefit for classical methods, whereas transfer learning substantially improved performance under limited-data conditions. Finally, we outline how these models integrate into a real-time Data-Driven Decision Support System for automated waste sorting, highlighting potential reductions in landfill use and lifecycle environmental impacts.)

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