We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
AI summary Read the abstract
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.
The terminal settling velocity of microplastics plays a vital role in the physical behavior of microplastics, and is related to the migration and fate of these microplastics in the ocean. At present, the terminal settling velocity is mostly calculated by formulae, which also leads to a fewer studies on the use of machine-learning models to predict its settling velocity in this field. This study fills this gap by studying the prediction of the settling velocity by machine-learning models and compares it with the traditional formula calculation method. This study evaluates three machine-learning models, namely, random forest, linear regression, and the back propagation neural network. The results of this study show that the prediction results of the three machine-learning models are more accurate than those of traditional formula calculations, with an accuracy increase of 12.79% (random forest), 9.3% (linear regression), and 13.92% (back propagation neural network), respectively. At the same time, according to the results of this study, random forest is better than the other models in the mean absolute error and root mean square error evaluation indicators, which are only 0.0036 and 0.0047. This paper proposes three machine-learning methods to prove that the prediction effect of machine learning is much better than traditional formula calculations, thereby improving the shortcomings in this field. At the same time, it also provides reliable data support for studying the migration behavior of microplastics in water bodies.
More Papers Like This
Machine learning-based prediction for settling velocity of microplastics with various shapes
AI summary Read the abstract
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.
Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers
AI summary Read the abstract
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.
A new modeling approach for microplastic drag and settling velocity
AI summary Read the abstract
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.
Dynamic prediction of large spherical and cylindrical microplastic deposition: a machine learning approach for transport and deposition
AI summary Read the abstract
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.
A new model for the terminal settling velocity of microplastics
AI summary Read the abstract
A new empirical model for the terminal settling velocity of microplastics was developed and validated using 1,343 experimental measurements covering a range of particle shapes and materials. The model improves predictions of microplastic sedimentation rates, which are critical for understanding how plastic particles are transported and deposited in water bodies.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.