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Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous

Heather L Wander1, Mary Jade Farruggia2, Sofia La Fuente3

  • 1Virginia Tech, Blacksburg, Virginia 24060, United States.

Environmental Science & Technology
|March 6, 2024
PubMed
Summary

Knowledge-guided machine learning (KGML) classified lake and reservoir surface area changes across the US. This approach provides ecologically interpretable patterns, moving beyond the "black-box" limitations of traditional machine learning.

Keywords:
K-means clusteringKGMLdomain knowledgelimnologymachine learningsurface areatemporal change

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Area of Science:

  • Environmental science
  • Hydrology
  • Remote sensing
  • Machine learning applications

Background:

  • Lake and reservoir surface areas are critical indicators of freshwater availability.
  • Machine learning (ML) and remote sensing data enable large-scale analysis of waterbody dynamics.
  • Interpreting ML models for ecological meaning remains a significant challenge due to their 'black-box' nature.

Purpose of the Study:

  • To characterize temporal patterns in lake and reservoir surface area changes from 1984 to 2016 for over 100,000 waterbodies in the contiguous US.
  • To employ knowledge-guided machine learning (KGML) to classify waterbodies into ecologically interpretable groups based on surface area change patterns.
  • To overcome the limitations of traditional ML by providing ecologically meaningful interpretations of waterbody dynamics.

Main Methods:

  • Utilized machine learning (ML) to analyze temporal patterns of lake and reservoir surface area change.
  • Applied knowledge-guided machine learning (KGML) to classify waterbodies into seven distinct, ecologically interpretable groups.
  • Analyzed data from 103,930 waterbodies across the contiguous United States spanning the years 1984–2016.

Main Results:

  • Identified distinct temporal patterns of surface area change for numerous lakes and reservoirs.
  • Classified 43% of waterbodies as having 'no change' in surface area.
  • Assigned the remaining 57% of waterbodies to groups exhibiting various linear and nonlinear patterns of surface area change.

Conclusions:

  • Knowledge-guided machine learning (KGML) successfully provides ecologically interpretable classifications of waterbody surface area dynamics.
  • This approach enhances the understanding of complex environmental processes driving changes in freshwater resources.
  • KGML offers a powerful framework for integrating ecological knowledge with data-driven pattern recognition in environmental studies.