AIMC Topic: Waste Management

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Prediction of municipal solid waste generation using nonlinear autoregressive network.

Environmental monitoring and assessment Nov 17, 2015
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generati...
Population Growth Gross Domestic Product Solid Waste Algorithms Waste Management Forecasting Neural Networks, Computer Malaysia Developing Countries Models, Theoretical Environmental Monitoring
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Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

Waste management (New York, N.Y.) Oct 9, 2015
Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verifi...
Waste Management Seasons Models, Theoretical Regression Analysis Iran Multivariate Analysis Neural Networks, Computer Solid Waste
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Managing waste for production of low-carbon concrete mix using uncertainty-aware machine learning model.

Environmental research Aug 15, 2025
This study introduces an uncertainty-aware AI-driven optimization framework for designing sustainable concrete mixtures that incorporate waste-derived materials. The primary objectives are to reduce global warming potential (GWP) and promote a circul...
Waste Management Uncertainty Carbon Construction Materials Global Warming Machine Learning
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Enhancing waste recognition with vision-language models: A prompt engineering approach for a scalable solution.

Waste management (New York, N.Y.) Aug 1, 2025
Conventional unimodal computer vision models, trained on limited bespoke waste datasets, face significant challenges in classifying waste images in material recovery facilities, where waste appears in diverse forms. Maintaining performance of these m...
Models, Theoretical Waste Management Image Processing, Computer-Assisted
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Automated Electro-construction waste Sorting: Computer vision for part-level segmentation.

Waste management (New York, N.Y.) Jul 15, 2025
The global generation of construction, demolition, and renovation (CDR) waste has surged, increasing the demand for efficient recycling solutions. Emerging technologies can automate the sorting of CDR waste, which is crucial for specialised categorie...
Recycling Waste Management Neural Networks, Computer Construction Industry Automation Electronic Waste
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Plastics detection and sorting using hyperspectral sensing and machine learning algorithms.

Waste management (New York, N.Y.) Jul 15, 2025
Plastic waste second life management requires effective detection (and sorting if necessary) techniques to tackle the environmental challenge it poses. This research explores the application of hyperspectral imaging in the spectral range 900-1700 nm ...
Algorithms Principal Component Analysis Machine Learning Recycling Waste Management Refuse Disposal Discriminant Analysis Hyperspectral Imaging Remote Sensing Technology Plastics
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An analytic hierarchy process combined with artificial neural network model to evaluate sustainable sludge treatment scenarios.

Waste management (New York, N.Y.) Jun 15, 2025
Sludge management in China faces critical environmental, economic, and technical challenges, necessitating urgent optimal management strategy selection. Given the limited number of comprehensive studies on sludge management, quantitative decision-mak...
Sewage Waste Management Waste Disposal, Fluid Neural Networks, Computer Models, Theoretical China
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A Systematic Review of AI-Based Techniques for Automated Waste Classification.

Sensors (Basel, Switzerland) May 18, 2025
Waste classification is a critical step in waste management that is time-consuming and necessitates automation to replace traditional approaches. Recently, machine learning (ML) and deep learning (DL) have gained attention from researchers seeking to...
Automation Waste Products Waste Management Machine Learning Deep Learning Artificial Intelligence Humans Neural Networks, Computer
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Transforming urban waste collection inventory: AI-Based container classification and Re-Identification.

Waste management (New York, N.Y.) May 15, 2025
This work lays the groundwork for creating an automated system for the inventory of urban waste elements. Our primary contribution is the development of, to the best of our knowledge, the first re-identification system for urban waste elements that u...
Artificial Intelligence Spain Cities Waste Management
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