Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management
[ 1 ] Instytut Logistyki, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee
2024
Journal year: 2024 | Journal volume: vol. 133. Part F
scientific article
english
- Disaster waste classification
- Artificial intelligence-enhanced system
- Optimization-driven
- Municipal waste management
- Environmental sustainability
EN This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by the increasing frequency and intensity of natural disasters like flooding. Traditional waste systems often struggle with the volume and heterogeneity of disaster waste, highlighting the need for innovative solutions. In this study, we present a novel disaster waste classification model integrating advanced artificial intelligence (AI) and optimization techniques to streamline waste categorization in post-disaster environments. Our approach leverages a dual ensemble deep learning framework. The first ensemble combines various image-segmentation methods, while the second integrates outputs from diverse convolutional neural network architectures. A modified artificial multiple intelligence system serves as a decision fusion strategy, enhancing accuracy at both ensemble points. We rigorously evaluated our model using three datasets: the “TrashNet” dataset for benchmarking against existing methods, as well as two meticulously curated, real-world datasets collected from flood-affected areas in Thailand. The results demonstrate that our method outperforms existing algorithms like VGG19, YoloV5, and InceptionV3 in general solid waste classification, achieving an average improvement of 11.18%. Regarding disaster waste specifically, our model achieves 96.48% and 96.49% accuracy on the curated datasets, consistently outperforming ResNet-101, DenseNet-121, and InceptionV3 by an average of 3.47%. These findings demonstrate the potential of our AI-enhanced model to revolutionize disaster waste management practices. Thus, we advocate integrating such technologies into municipal waste management policies to enhance resilience and optimize disaster responses. Future research will explore scaling the model to diverse disaster types and incorporating real-time data for adaptable waste management strategies.
30.05.2024
108614-1 - 108614-21
Article number: 108614
CC BY-NC-ND (attribution - noncommercial - no derivatives)
czasopismo hybrydowe
final published version
30.05.2024
at the time of publication
public
140
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