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Papers
61,005 resultsShowing papers similar to GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities
ClearA hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic
Researchers combined machine learning forecasting with mathematical supply-chain optimization to model municipal solid waste management in New York City under COVID-19 conditions, revealing trade-offs between economic efficiency and landfill diversion that could inform planning for future pandemic scenarios.
Smart Bin and IoT: A Sustainable Future for Waste Management System in Nigeria
Researchers proposed a smart waste bin system using Internet of Things technology to improve waste management in Nigerian cities. The system uses sensors and Wi-Fi connectivity to monitor bin fill levels remotely, enabling more efficient waste collection routes. The study highlights how affordable IoT-based solutions could help developing nations reduce plastic waste accumulation and environmental pollution.
Machine learning approach for automated beach waste prediction and management system: A case study of Mumbai
Researchers developed a machine learning system to predict beach waste generation patterns in Mumbai, aiming to enable more effective and automated waste management for one of the world's most polluted coastal cities.
AI Techniques Aid for Optimizing the Collection System of Industrial Plastic Waste
This study applied artificial intelligence techniques to optimize collection routes and predict demand for industrial plastic waste pickup. AI methods outperformed traditional statistical approaches in accuracy and route efficiency. Smarter collection systems could significantly reduce costs and improve recovery rates for industrial plastic waste.
Artificial intelligence for waste management in smart cities: a review
Researchers reviewed how artificial intelligence (AI) is being applied to nearly every aspect of waste management, from sorting recyclables with up to 99.95% accuracy to cutting transportation costs by over 36%. Their findings show AI could dramatically improve how cities handle plastic and other waste, reducing pollution and public health burdens.
A Smart Garbage Classification based on Deep Learning
Researchers developed an AI-powered garbage classification system using deep learning to automatically sort waste categories. Accurate automated waste sorting could improve plastic recycling rates, reducing the amount of plastic that eventually breaks down into environmental microplastics.
Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review
Researchers reviewed the full chain of municipal solid waste management — from source separation through collection, pretreatment, and valorization — finding that AI and the Internet of Things are emerging as powerful tools for optimizing collection routing and sorting efficiency within circular waste management systems.
An Automatic Garbage Classification System Based on Deep Learning
Researchers developed an automated garbage classification system using a deep learning algorithm based on ResNet-34, achieving 99% classification accuracy with a processing time of under one second per item. Automated waste sorting technology like this could improve the efficiency of plastic waste recovery and reduce mismanaged plastic that eventually becomes environmental pollution.
Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems
Researchers proposed an autonomous framework for managing littered face masks in smart cities, combining a deep neural network trained on a novel dataset — achieving 96% detection accuracy at ten times the speed of comparable models — with location intelligence to predict high-risk litter zones and guide emergency response.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Autonomous Waste Classification Using Multi-Agent Systems and Blockchain: A Low-Cost Intelligent Approach
This study developed and tested a prototype smart waste bin using multi-agent systems and blockchain to automatically sort waste (organic, plastic, paper) in real time, demonstrating a low-cost approach for intelligent municipal waste management.
Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment
Researchers developed an AI-powered surveillance system called SAWN that uses video cameras and deep learning models to detect public littering by vehicles and pedestrians in real time, achieving up to 99.5% accuracy — offering a scalable tool to reduce plastic pollution at its source.
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
This study analyzed global plastic waste management practices and used artificial intelligence models to predict future waste trends. The researchers found that current waste management systems are struggling to keep up with rising plastic production, posing threats to ecosystems, human health, and the economy. The AI models help forecast where waste generation is headed, which could inform better policy decisions.
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
This paper is not about microplastics; it presents an IoT-based framework for intelligent energy management in smart cities, using machine learning to monitor and optimize building energy consumption.
The predictive model for COVID-19 pandemic plastic pollution by using deep learning method
Researchers built a deep learning model to predict how pandemic-related plastic waste — masks, gloves, and sanitizer bottles — would spread as pollution across Iranian megacities during COVID-19. Their neural network outperformed six other modeling methods, offering a tool for governments to manage hazardous plastic waste during future health crises.
Medical waste management during coronavirus disease 2019 pandemic at the city level
Researchers developed an integrated medical waste management model incorporating uncertain waste generation estimates and pickup routing optimization for COVID-19-related infectious waste at the city level, applying optimistic, realistic, and pessimistic scenarios to guide waste treatment center placement and route planning in Turkey.
Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM)
This study introduced a hybrid modeling approach combining artificial neural networks (ANN) with hidden Markov models (HMM) for predicting microplastic pollution distribution in the environment. The ANN-HMM model outperformed single-method approaches for predicting spatial and temporal microplastic concentrations, offering an improved tool for environmental management and pollution forecasting.
Evaluation of formal waste reduction facility location compared to recyclable plastic waste generation in Denpasar City, Bali, Indonesia
Researchers modeled the spatial distribution of recyclable plastic waste generation across 200 households in Denpasar City, Bali, using six machine learning algorithms, with a Light Gradient Boosting Machine (LGBM) model achieving an R2 of 0.954 on test data. Spatial analysis of formal waste reduction facility coverage revealed only 32% area coverage and 46% capacity utilization, indicating major gaps in the city's waste management infrastructure.
A Systematic Review of Solid Waste Management (SWM) and Artificial Intelligence approach
This systematic review found that artificial intelligence and machine learning are increasingly being applied to solid waste management for tasks like waste classification, collection route optimization, and landfill monitoring. AI-based approaches showed significant improvements over traditional methods in sorting accuracy and operational efficiency.
Spatiotemporal graph neural networks for analyzing the influence mechanisms of river hydrodynamics on microplastic transport processes
Spatiotemporal graph neural networks were applied to model how microplastic contamination spreads across connected water bodies over time. This AI-driven modeling approach can improve real-time prediction and management of microplastic pollution in river and lake networks.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
Modeling of daily groundwater level using deep learning neural networks
Researchers applied a CNN-biLSTM deep learning model to predict daily groundwater levels, finding it outperformed conventional modeling approaches by capturing both spatial patterns and temporal dependencies in the data. The method offers improved accuracy for groundwater monitoring, which is critical for managing increasingly stressed freshwater resources.
Detection of Plastic Waste in Ocean Using Machine Learning Based Bi- LSTM With Triplet Attention Mechanism
Researchers developed a machine learning model using a bidirectional LSTM architecture with triplet attention mechanism to detect plastic waste in ocean environments, addressing the challenge of tracking plastic flow from rivers into marine ecosystems.