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61,005 resultsShowing papers similar to Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
ClearPredicting microplastic transport in open channels with different bed types and river regulation with machine learning techniques
Researchers studied microplastic transport in open channel flows with different bed types (acrylic, sand, cobble) and river regulation structures (weirs, sluice gates) using laboratory flumes. Sluice gates retained fewer microplastics than weirs, and machine learning models outperformed traditional bivariate analysis for predicting retention across conditions.
Dynamic prediction of large spherical and cylindrical microplastic deposition: a machine learning approach for transport and deposition
Researchers developed a machine learning model combined with dimensionless analysis to predict the deposition patterns of spherical and cylindrical microplastics in aquatic environments. The model accounts for varied flow conditions and particle shapes to improve predictions of where microplastics settle in water bodies. The study offers a practical tool for pollution monitoring efforts by helping predict microplastic accumulation hotspots in rivers and oceans.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
AI-Driven Framework Development for Predictive Classification of Microplastic Concentration of Aquatic Systems in the United States
Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
This review examines how machine learning techniques including neural networks and random forests are being applied to microplastic detection, classification, and ecological risk assessment, demonstrating faster and more accurate results than traditional analytical methods. The authors identify data standardization and model interpretability as key challenges for broader adoption.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Machine-Learning-Accelerated Prediction of Water Quality Criteria for Microplastics
Researchers developed a machine learning framework to predict microplastic toxicity in aquatic organisms and derive water quality criteria for five common polymer types. The random forest model outperformed other algorithms, with particle size, density, and aquatic species group accounting for 72% of prediction variability. The study found that polystyrene and PET exhibited the greatest toxicity, and that microplastics were generally more toxic in freshwater than saltwater environments.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Machine learning-based prediction for settling velocity of microplastics with various shapes
Researchers developed machine learning models to predict the settling velocity of microplastics based on their size, density, and shape. They classified microplastic shapes into fiber, film, and fragment categories and identified the optimal shape parameter for each, achieving significantly better prediction accuracy than existing theoretical models. The study reveals that particle size has the greatest influence on settling velocity, which is important for understanding how microplastics move and distribute in water environments.
Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.
Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers
Researchers developed a machine learning model to predict the settling velocity of microplastics across different shapes, including fragments, films, and fibers. Unlike existing models limited to specific morphologies, this approach works universally across all three particle types. The study provides a more reliable tool for modeling how microplastics move through and deposit in aquatic environments.
Machine Learning to Predict the Adsorption Capacity for Microplastics
Researchers developed and compared three machine learning models — random forest, support vector machine, and artificial neural network — to predict microplastic/water partition coefficients (log Kd) for chemical pollutant adsorption, addressing the limited experimental data available on microplastic adsorption capacity in aquatic environments.
A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years
This comprehensive review analyzes over 170 studies on using machine learning to predict water quality, covering both individual pollutant indicators and overall water quality indices. The authors highlight key challenges including data acquisition, model uncertainty, and the need to incorporate water flow dynamics into predictions. While broadly focused on water quality, these predictive tools are relevant to microplastics research because they could help forecast microplastic concentrations in water systems based on environmental conditions.
A new modeling approach for microplastic drag and settling velocity
Researchers developed a novel machine learning-based modelling framework to predict drag coefficients and settling velocities for microplastics of varying shapes (1D, 2D, 3D, and mixed) in aquatic environments. The framework achieved coefficient of determination values of 0.86-0.95 for drag models, outperforming traditional theoretical and data-fitting approaches in both speed and accuracy.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers
Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
Machine Learning Prediction of Adsorption Behavior of Xenobiotics on Microplastics under Different Environmental Conditions
Researchers developed a machine learning model to predict how different xenobiotic chemicals adsorb onto microplastics under varying environmental conditions, providing a computational tool to assess microplastics as vectors for pollutant transport without requiring extensive laboratory experiments.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Recent trends in marine microplastic modeling and machine learning tools: Potential for long-term microplastic monitoring
This review examines recent advances in numerical modeling and machine learning tools for tracking marine microplastic transport, highlighting their potential for improving long-term microplastic monitoring and understanding environmental fate.