Automated machine learning achieves accurate water quality prediction with reduced parameter requirements

Accurate water quality assessment is critical for environmental monitoring and public health. Conventional Water Quality Index (WQI) computation methods, however, often rely on numerous parameters and labor-intensive processes, thus limiting their practicality for rapid assessments. While Machine Learning (ML) offers promising alternatives, the development of high-performing models typically demands extensive expertise and computational resources. This study addresses the latter gap by leveraging Automated Machine Learning (AutoML), specifically the AutoGluon platform, to predict WQI from a reduced set of readily available water quality parameters. Our objectives were to (i) evaluate the predictive performance…

• CD
  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
• CN
  Conselho Nacional de Desenvolvimento Científico e Tecnológico

  Awards: 2021 AV02 0062/22, APQ-04458-23, 307688/2022-4, BPD-00083-22, 409433/2022-5, 304646/2025-3, 304646/2025- 3, APQ-02513-22
• FD
  Financiadora de Estudos e Projetos

  Award: 2021 AV02 0062/22
• FD
  Fundação de Amparo à Pesquisa do Estado de Minas Gerais

  Award: APQ-04458-23
• UF
  Universidade Federal de Juiz de Fora