An AI-driven multimodal framework for automated waste segregation and energy recovery in smart cities: a case study for Saudi Vision 2030

Shadi Alshraah and Loubna Alajmi

Arab Gulf Journal of Scientific Research, 2026, 1-14

Abstract: PurposeThis study develops an AI-driven multimodal framework integrating automated waste segregation with energy recovery prediction to support Saudi Vision 2030 sustainability goals.Design/methodology/approachA two-stage vision pipeline (YOLOv9 + Swin Transformer) performs real-time waste detection and classification. Multimodal physicochemical features are modeled using XGBoost, deep neural networks (DNNs), and graph neural networks (GNNs) to predict energy recovery potential. Reinforcement learning (RL) optimizes routing of waste streams to appropriate facilities. Experiments use a Saudi-specific dataset of 50,000 annotated images and 5,000 physico-chemical records.FindingsThe integrated framework achieved mAP = 94.3% and R2 = 0.96, improving landfill diversion and renewable energy contribution by 12.1% and 15.2%, respectively, compared with baseline models.Research limitations/implicationsHazardous waste remains underrepresented in the dataset. Future work will address this via targeted data collection and active learning.Practical implicationsThe framework provides a deployable solution for real-time waste classification and energy estimation in smart-city contexts, with a projected payback period of approximately 1.5 years for a 1,000-bin deployment.Originality/valueThis study introduces the first Saudi-specific multimodal waste dataset and a unified AI framework bridging waste segregation, energy prediction, and smart-city optimization—an end-to-end solution absent from prior literature.

Keywords: Artificial intelligence; Waste segregation; Energy recovery; YOLOv9; Swin transformer; Graph neural networks; Reinforcement learning; Smart cities; Saudi Vision 2030 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eme:agjsrp:agjsr-10-2025-0187

DOI: 10.1108/AGJSR-10-2025-0187

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