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December 4, 2025Journal of HydroinformaticsOpen Access

Integrating principal component analysis and machine learning to assess riverine impacts on lake water quality: a case study of the Bilate River–Lake Abaya Watershed, Rift Valley, Ethiopia

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Authors

DHDereje Yonas HeranoTWTekalegn Ayele WoldesenbetSTSirak Tekleab

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Overview

Analysis demonstrates seasonal variations in water quality in Lake Abaya, suggesting wetland restoration and soil conservation may improve conditions.

Key Points

  • Water quality variations were influenced by river inputs from the Bilate River, particularly nutrients and sediments.
  • Statistical methods revealed significant seasonal differences in water quality parameters, pH and TDS among them.
  • Machine learning models, particularly random forest, predicted key parameters affecting ecosystem health effectively.
  • Restoration of wetlands is crucial, as outcomes indicate agricultural impacts detrimentally affect water quality.

Cite This Study

Herano et al. (2025) studied this question.

synapsesocial.com/papers/694023fa2d562116f28fdcc4https://doi.org/10.2166/hydro.2025.085
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