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Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

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RiSIM: River surface image monitoring software for quantifying floating macroplastic transport.

Tomoya Kataoka1, Takushi Yoshida2, Kenji Sasaki2

  • 1Department of Civil & Environmental Engineering, Ehime University, Matsuyama, Japan; Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan.

Water Research
|September 30, 2025
PubMed
Summary

We developed River Surface Image Monitoring Software (RiSIM) to track floating plastic in rivers using AI. RiSIM accurately quantifies plastic transport, aiding in waste management and mitigation strategies.

Keywords:
AIHydrologyJapanMarine litterPlastic pollutionRemote sensing

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Continuous plastic monitoring in rivers is crucial for understanding plastic pollution.
  • Image-based monitoring with deep learning offers an effective strategy for observing floating plastic transport.

Purpose of the Study:

  • To develop and validate River Surface Image Monitoring Software (RiSIM) for quantifying floating macroplastic transport.
  • To assess the accuracy of RiSIM under various river conditions, including floods.

Main Methods:

  • Utilized template matching for frame identification and deep learning models for plastic detection, classification, and tracking.
  • Employed mark-release-recapture experiments and in-situ visual observations for validation.
  • Analyzed plastic transport rates in terms of quantity and mass, correlating them with river discharge.

Main Results:

  • RiSIM demonstrated high agreement with ground truth data for plastic transport rates (r = 0.91 for quantity, r = 0.80 for mass).
  • The software accurately captured temporal variations in plastic transport, even during flood events (r = 0.87).
  • Significant relationships were found between daily plastic transport rates and river discharge during floods (r² = 0.36 for quantity, r² = 0.27 for mass).

Conclusions:

  • RiSIM is a powerful near-field remote sensing tool for quantifying riverine plastic transport.
  • The software provides valuable data for managing mismanaged plastic waste in aquatic environments.
  • RiSIM's ability to assess flood-induced plastic transport aids in targeted mitigation efforts.