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A data-mining framework for exploring the multi-relation between fish species and water quality through

Wen-Ping Tsai1, Shih-Pin Huang2, Su-Ting Cheng1

  • 1Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC.

The Science of the Total Environment
|November 22, 2016
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Summary

Riverine fish communities are linked to water quality using artificial neural networks (ANNs). Key indicators like dissolved oxygen (DO) and total phosphorus (TP) help classify fish into distinct eco-water quality groups, aiding conservation efforts.

Keywords:
Artificial neural network (ANN)Eco-hydrological environmentsFish communityFlow regimeSelf-organizing map (SOM)Water quality

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Area of Science:

  • Eco-hydrology
  • Environmental Science
  • Computational Ecology

Background:

  • River water quality in Taiwan fluctuates significantly during typhoon seasons, impacting aquatic ecosystems.
  • Understanding the relationship between fish communities and water quality is crucial for managing riverine environments.

Purpose of the Study:

  • To investigate the relationship between fish communities and water quality parameters in northern Taiwan's rivers.
  • To utilize artificial neural networks (ANNs) for analyzing eco-hydrological systems and identifying key environmental indicators.

Main Methods:

  • Collected 276 datasets including 8 water quality parameters and 25 fish species from 10 sampling sites.
  • Employed Self-Organizing Feature Maps (SOM) for clustering, analysis, and visualization of heterogeneous data.
  • Utilized the Structuring Index (SI) to determine variable importance and identify indicator factors.

Main Results:

  • SOM successfully clustered fishery sampling sites, reflecting spatial characteristics.
  • Identified three distinct eco-water quality groups based on water quality parameters and fish species composition.
  • Dissolved oxygen (DO), total phosphorus (TP), and the fish species *Onychostoma barbatulum* were identified as key indicators.

Conclusions:

  • The developed methodology provides a cost-effective approach to link fish species with critical water quality factors.
  • Visualizing SOM topological maps reveals detailed interrelations between water quality and fish communities in stream habitats.
  • This approach serves as a valuable reference for comprehensive eco-hydrological management and conservation strategies.