Machine learning-based prediction for settling velocity of microplastics with various shapes
Abstract
Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R 2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = d n(g/v 2)1/3 > 80, where d n is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.
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SHAP is a game-theoretic approach that decomposes model predictions into additive feature contributions.
“The second method employed SHAP (SHapley Additive exPlanations), a game-theoretic approach that decomposes model predictions into additive feature contributions (Jia et al., 2024; Qian et al., 2024).”
From A two-step structural equation modeling and explainable machine learning framework for understanding university students’ adoption of generative AI: balancing intrinsic motivations and perceived risks (2026) · cites this paper as Qian et al., 2024
The machine learning process consists of three key steps: feature input, model construction, and performance evaluation.
“The machine learning process consists of three key steps: feature input, model construction, and performance evaluation (Qian et al., 2024; Qiu et al., 2025).”
From A two-step structural equation modeling and explainable machine learning framework for understanding university students’ adoption of generative AI: balancing intrinsic motivations and perceived risks (2026) · cites this paper as Qian et al., 2024
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- Authors
- Qian, S., Qiao, X., Zhang, W., Yu, Z., Dong, S., & Feng, J.
- Affiliations
- Hohai University (CN)
- University of Alberta (CA)
- Corresponding author
- Feng, Jiangang
- Venue
- Water Research
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 249
- Pages
- 121001
- ISSN
- 0043-1354 (ISSN-L)
- OpenAlex
- W4389497996
- Cited by
- 76
- References
- 55
- Citations by year
- 2026: 23 · 2025: 31 · 2024: 22
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Water Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (42277001, 52279063)
- Topics
- Physical Sciences › Environmental Science › Pollution › Microplastics and Plastic Pollution
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Aquatic and Environmental Studies
- Keywords
- microplastics, terminal velocity, microplastic shape, shape parameter, sphericity, flatness, elongation, relative density, feature importance
- MeSH terms
- Microplastics, Environmental Monitoring, Plastics, Water Pollutants, Chemical
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