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Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality

Miljan Kovačević1, Bahman Jabbarian Amiri2, Silva Lozančić3

  • 1Faculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, Serbia.

Toxics
|December 22, 2023
PubMed
Summary

Machine learning models effectively predict water quality parameters by analyzing catchment features. The Random Forest model demonstrated superior performance in assessing various water quality indicators for sustainable water management.

Keywords:
geological permeabilityhydrologic soil groupsland coverland usemachine learningwater quality

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Effective water quality management requires understanding the links between catchment physical attributes and in-stream water quality.
  • Catchment characteristics like geological permeability and soil types influence water quality parameters.

Purpose of the Study:

  • To evaluate the efficacy of machine learning models in predicting key water quality parameters.
  • To identify the significance of individual input variables in water quality prediction.
  • To establish relationships between catchment features and 11 in-stream water quality parameters.

Main Methods:

  • Utilized 5-year (1998-2002) water quality data from 88 stations in Iran.
  • Applied machine learning methods to predict parameters including SAR, Na+, Mg2+, Ca2+, SO42-, Cl-, HCO3-, K+, pH, EC, and TDS.
  • Evaluated model performance using Pearson's Linear Correlation Coefficient (R) and Mean Absolute Percentage Error (MAPE).

Main Results:

  • The Random Forest (RF) model exhibited the best performance across multiple water quality parameters.
  • Identified significant relationships between catchment characteristics and in-stream water quality.
  • Provided predictive models for 11 critical water quality indicators.

Conclusions:

  • Machine learning, particularly RF, offers a robust approach for predicting water quality parameters.
  • Findings support targeted strategies for environmental sustainability and resilient water ecosystems.
  • This research provides a practical framework for balancing human activities with water resource preservation.