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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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Machine learning in natural and engineered water systems.

Ruixing Huang1, Chengxue Ma1, Jun Ma2

  • 1Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China.

Water Research
|September 24, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) offers efficient solutions for water management, predicting water quality, and identifying contaminants. This review details ML applications in natural and engineered water systems, aiding water conservation and intelligent development.

Keywords:
Engineered water systemsMachine learningNatural water systems

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Efficient water management is crucial for survival and development.
  • Machine learning (ML) shows high efficiency in solving water-related problems.
  • ML predicts water quality, identifies contaminants, and aids water system management.

Purpose of the Study:

  • To review ML applications in natural and engineered water systems.
  • To introduce ML concepts and modeling steps.
  • To analyze ML algorithms and propose future directions.

Main Methods:

  • Data preparation, algorithm selection, and model evaluation.
  • Review of recent studies on ML applications in water science.
  • Analysis of ML algorithm advantages and disadvantages.

Main Results:

  • ML effectively predicts water quality indicators and identifies contaminants using image recognition.
  • Applications include groundwater contaminant mapping, water resource classification, and pollutant toxicity evaluation.
  • ML models treatment techniques, aids in drinking water purification, and optimizes sewage treatment.

Conclusions:

  • ML provides practical, reliable, and efficient solutions for diverse water management challenges.
  • Algorithm selection depends on specific study requirements.
  • ML is poised to drive intelligent development in water science.