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Predicting microplastic quantities in Indonesian provincial rivers using machine learning models.

Aan Priyanto1, Dian Ahmad Hapidin2, Dhewa Edikresnha2

  • 1Research Group of Physics and Technology of Advanced Materials, Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung, Jawa Barat 40132, Indonesia; Doctoral Program of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung, Jawa Barat 40132, Indonesia.

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Summary

Machine learning models accurately predicted microplastic levels in Indonesian rivers. The Tree algorithm showed the best performance, identifying temperature, GDP, and population density as key factors influencing pollution.

Keywords:
Anthropogenic factorsEnvironmental factorsIndonesia riversMachine learningMicroplasticsPredictive modeling

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Area of Science:

  • Environmental Science
  • Data Science
  • Ecotoxicology

Background:

  • Microplastic pollution is a significant global environmental and health issue.
  • Freshwater systems are crucial pathways for microplastic transport.
  • Effective monitoring strategies are needed to manage microplastic contamination.

Purpose of the Study:

  • To evaluate machine learning models for predicting microplastic concentrations in Indonesian rivers.
  • To identify key environmental and anthropogenic factors driving microplastic abundance.
  • To provide data-driven insights for mitigating freshwater microplastic pollution.

Main Methods:

  • Applied multiple machine learning algorithms: Tree, k-Nearest Neighbors (kNN), Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), and Neural Networks (NN).
  • Utilized environmental and anthropogenic data across Indonesia's 24 provinces.
  • Validated model performance using coefficient of determination (R²) and mean absolute percentage error (MAPE).

Main Results:

  • The Tree algorithm demonstrated superior predictive performance with R² = 0.838 and MAPE = 0.242.
  • Annual average temperature, GDP per capita, and population density were identified as significant predictors of microplastic concentration.
  • The study confirmed the efficacy of machine learning in analyzing complex environmental data.

Conclusions:

  • Machine learning, particularly the Tree algorithm, offers a powerful tool for predicting and monitoring microplastic pollution in freshwater systems.
  • Understanding the influence of socioeconomic and climatic factors is vital for targeted pollution control.
  • This research supports the development of informed environmental management strategies for Indonesian rivers and beyond.